The DOI matches its Crossref record, with a small difference in title or year; 31 of the 31 references it cites were found in a registry or an index; 2 of the 14 sources its claims cite were checked for support.
ImageNet classification with deep convolutional neural networks
Open accessAbstract
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
42 Own result · 21 Own interpretation · 7 No basis given · 4 No full text of the sources · 3 Sources not found · 2 Supported · 2 Not checked yet · 1 Own method
Every source the paper cites for this claim was checked and supports it.
In the paper
“This technique reduces complex co adaptations of neurons, since a neuron cannot rely on the presence of particular other neurons.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
What the sources say
- Supportedcites [12] 12. Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012). source
The paper states on p. 1 that dropout prevents complex co-adaptations by randomly omitting hidden units, so a hidden unit cannot rely on other hidden units being present. This directly supports the citing sentence. (LLM, conf=0.95)
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“There is, however, a very efficient version of model combination that only costs about a factor of two during training.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
What the sources say
- Supportedcites [12] 12. Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012). source
The paper describes dropout as an efficient way to combine many neural-network models (p. 1). In F.6 Training (p. 17), it reports that ImageNet training took roughly four days with dropout versus two days without it, supporting the approximate factor-of-two training cost. (LLM, conf=0.88)
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This scheme reduces our top-1 and top-5 error rates by 1.7% and 1.2%, respectively, as compared with a net with half as many kernels in each convolutional layer trained on one GPU.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.2. Training on multiple GPUs
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The size of our network made overfitting a significant problem, even with 1.2 million labeled training examples”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“So these data augmentation schemes are, in effect, computationally free.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we found that removing any convolutional layer (each of which contains no more than 1% of the model's parameters) resulted in inferior performance.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This scheme reduces the top-1 and top-5 error rates by 0.4% and 0.3%, respectively, as compared with the non overlapping scheme s = 2, z = 2, which produces output of equivalent dimensions.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This increases the size of our training set by a factor of 2048”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Deep CNNs with ReLUs train several times faster than their equivalents with tanh units.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.1. Rectified Linear Unit nonlinearity
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, we still find that the following local normalization scheme aids generalization.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.3. Local response normalization
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 18
- Probably not a substantial claim. 26
- The quote only partly states this claim. 2
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although the 1000 classes of ILSVRC make each training example impose 10 bits of constraint on the mapping from image to label, this turns out to be insufficient to learn so many parameters without considerable overfitting.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5. REDUCING OVERFITTING
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“though the resulting training examples are, of course, highly inter dependent.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
plainstates an association- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively”
§ ImageNet Classification with Deep Convolutional Neural Networks > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We generally observe during training that models with overlapping pooling find it slightly more difficult to overfit.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
plainstates a possible cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“a four-layer CNN achieved a 13% test e r r o r r a t e w i t h o u t n o r m a l i z a t i o n a n d 1 1 % w i t h normalization.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.3. Local response normalization
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“so we used several effective techniques for preventing overfitting”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Response normalization reduces our top-1 and top-5 error rates by 1.4% and 1.2%, respectively.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.3. Local response normalization
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The two-GPU net takes slightly less time to train than the one-GPU net.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.2. Training on multiple GPUs
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our network contains a number of new and unusual features which improve its performance and reduce its training time”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Without this scheme, our network suffers from substantial overfitting, which would have forced us to use much smaller networks.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
hedgedstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, only other types of cat are considered plausible labels for the leopard.”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This initialization accelerates the early stages of learning by providing the ReLUs with positive inputs.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 6. DETAILS OF LEARNING
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Notice that at the pixel level, the retrieved training images are generally not close in L2 to the query images in the first column.”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Training one CNN, with an extra sixth convolutional layer over the last pooling layer, to classify the entire ImageNet Fall 2011 release (15M images, 22K categories), and then”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our top-1 and top-5 error rates on this dataset are 67.4% and 40.9%, attained by the net described above but with an additional, sixth convolutional layer over the last pooling layer.”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The Fall 2009 ImageNet split used by the authors, which differs from previous authors' splits, does not affect the results appreciably.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This kind of specialization occurs during every run and is independent of any particular random weight initialization (modulo a renumbering of the GPUs).”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The magnitude of the effect demonstrated here varies with network architecture, but networks with ReLUs consistently learn several times faster than equivalents with saturating neurons.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 9. EPILOGUE
boostedstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our network achieves top-1 and top-5 test set error rates of 37.5% and 17.0%, respectively.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 7. RESULTS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Averaging the predictions of five similar CNNs gives an error rate of 16.4%.”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the remainder of this paragraph, we use validation and test error rates interchangeably because in our experience they do not differ by more than 0.1% (see Table 2).”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Notice that even off-center objects, such as the mite in the top-left, can be recognized by the net.”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“All of our experiments suggest that our results can be improved simply by waiting for faster GPUs and bigger datasets to become available.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
hedgedstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ReLUs have the desirable property that they do not require input normalization to prevent them from saturating.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.3. Local response normalization
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Convolutional neural networks (CNNs) constitute one such class of models.9,15,17,19,21,26,32”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
What the sources say
- Not checked — no full text of the sourcecites [9] 9. Fukushima, K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36, 4 (1980), 193-202. source
- Not checked yetcites [15] 15. Krizhevsky, A. Convolutional deep belief networks on cifar-10. Unpublished manuscript, 2010.
- Not checked yetcites [17] 17. LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., Jackel, L., et al. Handwritten digit recognition with a back-propagation network. In Advances in Neural Information Processing Systems (1990).
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, the immense complexity of the object recognition task means that this problem cannot be specified even by a dataset as large as ImageNet, so our model should also have lots of prior knowledge to compensate for all the data we do not have.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
A statement the paper makes itself — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, compared to standard feedforward neural networks with similarly sized layers, CNNs have much fewer connections and parameters and so they are easier to train, while their theoretically best performance is likely to be only slightly worse.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
hedgedstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“methods that require careful hand-engineering by a programmer who understands the domain do not scale as well as methods that replace the programmer with a powerful general-purpose learning procedure.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 1. PROLOGUE
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Luckily, current GPUs, paired with a highly optimized implementation of 2D convolution, are powerful enough to facilitate the training of interestinglylarge CNNs”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“they also make strong and mostly correct assumptions about the nature of images (namely, stationarity of statistics and locality of pixel dependencies).”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Despite the attractive qualities of CNNs, and despite the relative efficiency of their local architecture, they have still been prohibitively expensive to apply in large scale to highresolution images.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“ImageNet, 7 which consists of over 15 million labeled high-resolution images in over 22,000 categories.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
What the sources say
- Not checked — no full text of the sourcecites [7] 7. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L. ImageNet: A large-scale hierarchical image database. In CVPR09 (2009). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“which is considerably better than the previous state-of-the-art”
§ ImageNet Classification with Deep Convolutional Neural Networks > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With enough computation and enough data, learning beats programming for complicated tasks that require the integration of many different, noisy cues.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 1. PROLOGUE
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“and recent datasets such as ImageNet contain enough labeled examples to train such models without severe overfitting.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“for deep neural networks to shine, they needed far more labeled data and hugely more computation.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 1. PROLOGUE
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If at least some training examples produce a positive input to a ReLU, learning will happen in that neuron.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.3. Local response normalization
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To learn about thousands of objects from millions of images, we need a model with a large learning capacity.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The constants k, n, α, and β are hyperparameters whose values are determined using a validation set”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.3. Local response normalization
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, on this dataset the primary concern is preventing overfitting, so the effect they are observing is different from the accelerated ability to fit the training set which we report when using ReLUs.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.1. Rectified Linear Unit nonlinearity
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“since then the best published results are 45.7% and 25.7% with an approach that averages the predictions of two classifiers trained on Fisher Vectors (FVs) computed from two types of densely sampled features.29”
§ ImageNet Classification with Deep Convolutional Neural Networks > 7. RESULTS
What the sources say
- Not checked — no full text of the sourcecites [29] 29. Sánchez, J., Perronnin, F. High- dimensional signature compression for large-scale image classification. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011 (2011). IEEE, 1665-1672. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Combining the predictions of many different models is a very successful way to reduce test errors, 1, 3”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
What the sources say
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A statement the paper makes itself — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“but it appears to be too expensive for big neural networks that already take several days to train.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
hedgedstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“The best published results on this dataset are 78.1% and 60.9%.23”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
What the sources say
- Not checked — no full text of the sourcecites [23] 23. Mensink, T., Verbeek, J., Perronnin, F., Csurka, G. Metric learning for large scale image classification: Generalizing to new classes at near-zero cost. In ECCV -European Conference on Computer Vision (Florence, Italy, Oct. 2012). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
Objects in realistic settings exhibit considerable variability.
“But objects in realistic settings exhibit considerable variability”
The paper relies on this without support.
Object identity is invariant to changes in the intensity and color of illumination.
“object identity is invariant to changes in the intensity and color of the illumination.”
The paper relies on this without support.
The Fall 2009 ImageNet split used by the authors, which differs from previous authors' splits, does not affect the results appreciably.
“Since there is no established test set, our split necessarily differs from the splits used by previous authors, but this does not affect the results appreciably.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In the field of DL, the CNN is the most famous and commonly employed algorithm [30, 71–75].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [30]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Convolutional neural networks (CNNs), built for spatial data, have demonstrated performance comparable to that of specialists in cancer detection and tumor segmentation [15,16,17,18,19,20].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [16]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Deep CNN architectures with 8, even 22 layers [4], [33], can be useful even for CADe problems where the available training datasets are limited.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [4]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Fully convolutional neural networks (fCNNs) have provided state-of-the-art performance for object classification and image segmentation (1).”
From Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning (2018) · cites this paper as [1]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The first wave saw the emergence of deep learning as the dominant paradigm for analyzing high-dimensional biological data.”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [1]
Experimental — accuracy not yet measured
The cited source says what the claim attributes to it.
In the citing paper
“Krizhevesky et al. [30] first proposed AlexNet and consequently improved the CNN learning ability by increasing its depth and implementing several parameter optimization strategies.”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [30]
The cited source says what the claim attributes to it.
In the citing paper
“In deep CNN architecture, AlexNet is highly respected [30], as it achieved innovative results in the fields of image recognition and classification.”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [30]
The cited source says what the claim attributes to it.
In the citing paper
“Many CNN models, e.g. AlexNet [30], GoogleNet [103], and ResNet [37], have been trained on large datasets such as ImageNet for image recognition purposes.”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [30]
The cited source says what the claim attributes to it.
In the citing paper
“To avoid model-specific biases, two different AI models, AlexNet [22] and VGG16 [38], were selected for classification.”
From Study on the Helpfulness of Explainable Artificial Intelligence (2024) · cites this paper as [22]
The cited source says what the claim attributes to it.
In the citing paper
“In particular, the choice of this specific size is based on the fact that the AlexNet 34 (and also similar CNN architectures) has been shown to offer satisfactory performances with an input matrix of that size or very similar, albeit for different types of problems with respect to the one presented in this work (e.g., image classification).”
The cited source says what the claim attributes to it.
In the citing paper
“Jung et al. [21] found that a reduction in accuracy does not occur when using only four bits for both weight and activation parameters, even for the ImageNet dataset [32].”
From Zero-Centered Fixed-Point Quantization With Iterative Retraining for Deep Convolutional Neural Network-Based Object Detectors (2021) · cites this paper as [32]
The cited source says what the claim attributes to it.
In the citing paper
“Feature-based approaches [17,18,19] commonly employ networks pre-trained on ImageNet [20] to map original images into a more distinguishable feature space. These methods are typically characterized by low training costs and outstanding performance.”
From An Unsupervised Method for Industrial Image Anomaly Detection with Vision Transformer-Based Autoencoder (2024) · cites this paper as [20]
The cited source says what the claim attributes to it.
In the citing paper
“Over the past several years, the successes of deep learning on visual tasks has critically relied on convolutional neural networks [20, 16].”
From Do Vision Transformers See Like Convolutional Neural Networks? (2021) · cites this paper as [20]
The cited source says what the claim attributes to it.
In the citing paper
“A common perception metric is the Learned Perceptual Image Patch Similarity (LPIPS) which uses pre-trained deep neural network backbones such as VGG [9] and AlexNet [22].”
From MTReD: 3D Reconstruction Dataset for Fly-over Videos of Maritime Domain (2025) · cites this paper as [22]
The cited source says what the claim attributes to it.
In the citing paper
“Starting with the the success of AlexNet [5] in the ILSVRC challenge [6] in 2012, deep learning models have been outperforming traditional approaches on a number of computer vision problems.”
From Satellite Pose Estimation Challenge: Dataset, Competition Design, and Results (2020) · cites this paper as [5]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Real-time volume estimation has been achieved using fully convolutional neural networks (FCNNs) [11] to produce state-of-the-art performance in a range of medical imaging modalities [12].”
From Fully Automated 3-D Ultrasound Segmentation of the Placenta, Amniotic Fluid, and Fetus for Early Pregnancy Assessment (2021) · cites this paper as [11]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Unfortunately, it has been acknowledged that the performance of those hashing schemes may degrade significantly if the projected dimensions are imbalanced [4], [6], [13], i.e., their variances vary a lot.”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [6]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“When convolutional RBMs are stacked together, they form convolutional deep belief networks [22].”
From Multimodal emotion recognition using deep learning architectures (2016) · cites this paper as [22]
Experimental — accuracy not yet measuredIn the citing paper
“When a low-capacity model, such as AlexNet, is used for pre-training, little improvement can be obtained when compared to the Resnet50 family [26].”
From Self-supervised learning: A succinct review (2023) · cites this paper as [26]
In the citing paper
“Since its introduction by Krizhevsky et al. [7] in 2012, the CNN model has been successfully applied to a wide range of multimodal applications, such as image dehazing [239, 240] and human activity recognition [241].”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [7]
In the citing paper
“The AlexNet architecture was published in [4], achieved significantly improved performance over the other non-deep learning methods for ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [4]
In the citing paper
“focusing on AI and deep learning architectures that have already become the de facto approach.”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [101]
In the citing paper
“Since then, fueled by the revolutionary results of AlexNet [101] and incarnations of patch-based adaptations of Deep Boltzmann Machines and stacked autoencoders, deep learning based segmentation of anatomy and pathology has witnessed a revolution (see also Table II), where for some tasks now we observe human level performance [102].”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [101]
In the citing paper
“ReLU significantly increases the rate of con-vergence of stochastic gradient descent in comparison with the sigmoid [26].”
From Real-time system for automatic cold strip surface defect detection (2019) · cites this paper as [26]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
“we found that removing any convolutional layer (each of which contains no more than 1% of the model's parameters) resulted in inferior performance.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.5. Overall architecture
“The third convolutional layer has 384 kernels of size 3 × 3 × 256”
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,889
Self-citationLearning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,501
Self-citation(2010) · International Conference on Machine Learning · Cited by 13,995
Self-citationImproving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
Self-citationFukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759
LabelMe: A Database and Web-Based Tool for Image Annotation
Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0090-8 · Cited by 4,210
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196
Research works printed but not linked: 13
- Berg A., 2010, Large scale visual recognition challenge
- Deng J., 2009, CVPR09
- Krizhevsky A., 2009, Department of Computer Science
- Krizhevsky A., 2010, Convolutional deep belief networks on cifar-10. Unpublished manuscript
- Krizhevsky A., 2011, ESANN
- LeCun Y., 1990, Advances in Neural Information Processing Systems
- LeCun Y., 1985, Une procedure d'apprentissage pour reseau a seuil asymmetrique (a learning scheme for asymmetric threshold networks)
- 10.5555/1896300.1896315 · DOI
- Mensink T., 2012, Italy
- 10.5555/3104322.3104425 · DOI
- Rumelhart D.E., 1985, DTIC Document
- 10.5555/938980.939477 · DOI
- Werbos P., 1974, Beyond regression: New tools for prediction and analysis in the behavioral sciences
Cited by
Sorted by citations
Items 31,301–31,400 of 108,435
Wang, Y.-Y., Wang, W.-C., Xu, D., Zhao, Y., & Zang, H.-F. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.131482 · Cited by 27
ConvFishNet: An efficient backbone for fish classification from composited underwater images
Qu, H., Wang, G., Li, Y., Qi, X., & Zhang, M. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121078 · Cited by 28
Trojan Attack and Defense for Deep Learning-Based Navigation Systems of Unmanned Aerial Vehicles
Mynuddin, M., Khan, S. U., Ahmari, R., Landivar, L., Nabil, M., & Homaifar, A. (2024) · IEEE Access · doi:10.1109/access.2024.3419800 · Cited by 29
Medical image registration in the era of Transformers: A recent review
Ramadan, H., Bourakadi, D. E., Yahyaouy, A., & Tairi, H. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101540 · Cited by 30
Kızıloluk, S., Sert, E., Hammad, M. A., et al. (2024) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2024.06.006 · Cited by 26
Hamza, M. H., & Chattopadhyay, A. (2024) · Composite Structures · doi:10.1016/j.compstruct.2024.118360 · Cited by 27
A survey of multimodal federated learning: background, applications, and perspectives
Pan, H., Zhao, X., He, L., Shi, Y., & Lin, X. S. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01422-9 · Cited by 29
Chen, C., Chen, Z., Liu, D., Zhang, X., Gao, C., Shan, L., Liu, L., Chen, T., Guo, T., & Chen, H. (2024) · Matter · doi:10.1016/j.matt.2024.06.050 · Cited by 31
Fractional Calculus Meets Neural Networks for Computer Vision: A Survey
Coelho, C., Costa, M. F. P., & Ferrás, L. L. (2024) · AI · doi:10.3390/ai5030067 · Cited by 34
Xie, Z., Chen, J., Shi, Z., Liu, S., & He, S. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111824 · Cited by 31
Teng, J., Long, X., Yang, Q., Jing, G., & Liu, H. (2024) · Transportation Geotechnics · doi:10.1016/j.trgeo.2024.101334 · Cited by 29
Deep learning for 3D object recognition: A survey
Muzahid, A. A. M., Han, H., Zhang, Y., Li, D., Zhang, Y., Jamshid, J., & Sohel, F. A. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128436 · Cited by 27
Deep Reinforcement Learning for Integrated Sensing and Communication in RIS-Assisted 6G V2X System
Long, X., Zhao, Y. B., Wu, H., et al. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3449969 · Cited by 26
Detection of AI-Generated Synthetic Images with a Lightweight CNN
Lađević, A. L., Kramberger, T., Kramberger, R., & Vlahek, D. (2024) · AI · doi:10.3390/ai5030076 · Cited by 38
Wang, W., Chen, J., Han, G., Shi, X., & Qian, G. (2024) · Sensors · doi:10.3390/s24185944 · Cited by 29
Dual Prototype Attention for Unsupervised Video Object Segmentation
Cho, S., Lee, M., Lee, S., Lee, D., Choi, H., Kim, I.-J., & Lee, S. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01820 · Cited by 31
Wang, Y., Li, X., Wen, S., Zhang, G., Yue, H., Feng, H., Han, J., & Ding, E. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01516 · Cited by 35
Chen, T., Lv, L., Wang, D., Zhang, J., Yang, Y., Zhao, Z., Wang, C., Guo, X., Chen, H., Wang, Q., et al. (2024) · ACM Computing Surveys · doi:10.1145/3698589 · Cited by 53
Jangid, M., & Kumar, R. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20262-3 · Cited by 32
An improved ResNet method for urban flooding water depth estimation from social media images
Du, W., Qian, M., He, S., Xu, L., Zhang, X., Huang, M., & Chen, N. (2024) · Measurement · doi:10.1016/j.measurement.2024.116114 · Cited by 30
An, N. H., Yang, J. H., Song, E., Hwang, S.-H., Byun, H., & Park, S. (2024) · Sustainability · doi:10.3390/su16219482 · Cited by 31
Vision-based human action quality assessment: A systematic review
Liu, J., Wang, H., Stawarz, K., Li, S., Fu, Y., & Liu, H. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125642 · Cited by 36
An Intelligent Approach for Retinal Vessels Extraction Based on Transfer Learning
Verma, P. K., Kaur, J., & Singh, N. (2024) · SN Computer Science · doi:10.1007/s42979-024-03403-1 · Cited by 26
Baig, M. A. A., Ratyal, N. I., Amin, A., et al. (2024) · Future Internet · doi:10.3390/fi16120436 · Cited by 26
Photonic diffractive generators through sampling noises from scattering media
Zhan, Z., Wang, H., Liu, Q., & Fu, X. (2024) · Nature Communications · doi:10.1038/s41467-024-55058-4 · Cited by 29
Ultrasonic defect detection in a concrete slab assisted by physics-informed neural networks
Lee, S., & Popovics, J. S. (2024) · NDT & E International · doi:10.1016/j.ndteint.2024.103311 · Cited by 29
Kreouzi, M., Theodorakis, N., Feretzakis, G., Paxinou, E., Sakagianni, A., Kalles, D., Αναστασίου, Α., Verykios, V. S., & Nikolaou, M. (2024) · Cancers · doi:10.3390/cancers17010028 · Cited by 32
Limited-Data SAR ATR Causal Method via Dual-Invariance Intervention
Wang, C., Xu, R., Huang, Y., Pei, J., Huang, C., Zhu, W., & Yang, J. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3528464 · Cited by 31
Radar Signal Processing and Its Impact on Deep Learning-Driven Human Activity Recognition
Ayaz, F., Alhumaily, B., Hussain, S., Imran, M. A., Arshad, K., Assaleh, K. T., & Zoha, A. (2025) · Sensors · doi:10.3390/s25030724 · Cited by 31
Gür, Y. E., Toğaçar, M., & Solak, B. (2025) · Computational Economics · doi:10.1007/s10614-025-10893-5 · Cited by 35
Deep learning in crack detection: A comprehensive scientometric review
Wu, Y., Li, S., Li, J., Yu, Y., Li, J., & Li, Y. (2025) · Journal of Infrastructure Intelligence and Resilience · doi:10.1016/j.iintel.2025.100144 · Cited by 31
A Review of Artificial Intelligence Applications in Predicting Faults in Electrical Machines
Habyarimana, M., & Adebiyi, A. A. (2025) · Energies · doi:10.3390/en18071616 · Cited by 39
Ma, H., Li, G., Zhang, H., et al. (2025) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2025.137646 · Cited by 26
Srikanth, D. V., Sukumar, G. D., & Sobhan, P. V. S. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.116252 · Cited by 26
Billard, A., Albu‐Schäffer, A., Beetz, M., Burgard, W., Corke, P., Ciocarlie, M., Dahiya, R., Kragić, D., Goldberg, K., Nagai, Y., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01050-6 · Cited by 25
“…representatives of a diversity of tasks that robots may have to perform, and of environments they may encounter, to designing AI algorithms tailored specifically to robotics problems but generic enough to apply to a wide range of applications and transfer easily to a variety of robotic platforms.”
Kasim, S. S., Malek, S., Tang, J., Kiew, X. N., Cheen, S., Liew, B., Saidon, N., Ezman, R. S. R., & Shariff, R. E. R. (2025) · Scientific Reports · doi:10.1038/s41598-025-05585-x · Cited by 35
An improvement by introducing LBFGS idea into the Adam optimizer for machine learning
Zhang, Z., Yuan, G., Qin, Z., & Luo, Q. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129002 · Cited by 32
Unsupervised Feature Learning for RGB-D Image Classification
Jhuo, I., Gao, S., Zhuang, L., Lee, D. T., & Ma, Y. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16865-4_18 · Cited by 26
Deep learning, dark knowledge, and dark matter
Sadowski, P., Collado, J., Whiteson, D., & Baldi, P. (2014) · Cited by 29
Hybrid multi-layer deep CNN/aggregator feature for image classification
Kulkarni, P., Zepeda, J., Jurie, F., Pérez, P., & Chevallier, L. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178196 · Cited by 26
Large-scale Image Retrieval using Neural Net Descriptors
Novák, D., Batko, M., & Zezula, P. (2015) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2766462.2767868 · Cited by 26
Semantic Segmentation with Object Clique Potential
Qi, X., Shi, J., Liu, S., Liao, R., & Jia, J. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.297 · Cited by 22
Toward correlating and solving abstract tasks using convolutional neural networks
Peng, K., & Chen, T. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477616 · Cited by 28
Using Artificial Intelligence Techniques to Emulate the Creativity of a Portrait Painter
DiPaola, S., & McCaig, G. (2016) · Electronic workshops in computing · doi:10.14236/ewic/eva2016.32 · Cited by 25
Multimedia Hashing and Networking
Liu, W., & Zhang, T. (2016) · IEEE Multimedia · doi:10.1109/mmul.2016.39 · Cited by 24
Convolutional Neural Networks with Data Augmentation for Classifying Speakers’ Native Language
Keren, G., Deng, J., Pohjalainen, J., & Schuller, B. W. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-261 · Cited by 25
Improved scene identification and object detection on egocentric vision of daily activities
Vaca-Castano, G., Das, S., Sousa, J. P., Lobo, N. D. V., & Shah, M. A. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.10.016 · Cited by 27
The University of Passau Open Emotion Recognition System for the Multimodal Emotion Challenge
Deng, J., Cummins, N., Han, J., Xu, X., Ren, Z. X., Pandit, V., Zhang, Z., & Schuller, B. W. (2016) · Communications in computer and information science · doi:10.1007/978-981-10-3005-5_54 · Cited by 26
Data-driven light field depth estimation using deep Convolutional Neural Networks
Sun, X., Xu, Z., Meng, N., Lam, E. Y. M., & So, H. K. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727222 · Cited by 30
Object-Centric Representation Learning from Unlabeled Videos
Gao, R., Jayaraman, D., & Grauman, K. L. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54193-8_16 · Cited by 30
Efficient Imbalanced Multimedia Concept Retrieval by Deep Learning on Spark Clusters
Yan, Y., Chen, M., Sadiq, S. B., & Shyu, M. (2017) · International Journal of Multimedia Data Engineering and Management · doi:10.4018/ijmdem.2017010101 · Cited by 27
Empowering Simple Binary Classifiers for Image Set Based Face Recognition
Hayat, M., Khan, S. H., & Bennamoun, M. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1000-3 · Cited by 24
Supervised Deep Hashing for Hierarchical Labeled Data
Wang, D., Huang, H., Lu, C., Feng, B.-S., Wen, G., Nie, L., & Mao, X.-L. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12247 · Cited by 32
Biometric Spoofing Detection by a Domain-Aware Convolutional Neural Network
Gragnaniello, D., Sansone, C., Poggi, G., & Verdoliva, L. (2016) · International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) · doi:10.1109/sitis.2016.38 · Cited by 25
Anuran call classification with deep learning
Strout, J., Rogan, B., Seyednezhad, S. M. M., Smart, K., Bush, M. B., & Ribeiro, E. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952639 · Cited by 28
Bekele, E., Narber, C., & Lawson, W. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.55 · Cited by 26
Place Recognition: An Overview of Vision Perspective
Zeng, Z., Zhang, J., Wang, X., Chen, Y., & Zhu, C. (2018) · Applied Sciences · doi:10.3390/app8112257 · Cited by 25
Pedestrian detection with dilated convolution, region proposal network and boosted decision trees
Li, J., Wu, Y. X., Zhao, J., Guan, L., Ye, C., & Yang, T. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966367 · Cited by 27
Naito, T., Nagashima, Y., Taira, K., Uchio, N., Tsuji, S., & Shimizu, J. (2017) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2017.08.014 · Cited by 26
A Multiscale Deeply Described Correlatons-Based Model for Land-Use Scene Classification
Qi, K., Yang, C., Guan, Q., Wu, H., & Gong, J. (2017) · Remote Sensing · doi:10.3390/rs9090917 · Cited by 26
A Full Density Stereo Matching System Based on the Combination of CNNs and Slanted-Planes
Chen, L. F., Fan, L., Chen, J., et al. (2017) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2017.2767823 · Cited by 26
3D Convolutional Neural Network for Action Recognition
Zhang, J., Chen, L., & Tian, J. (2017) · Communications in computer and information science · doi:10.1007/978-981-10-7299-4_50 · Cited by 26
Huang, K., Jiang, H., & Zhang, X.-Y. (2017) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2017.2751062 · Cited by 28
Aurora image search with contextual CNN feature
Yang, X., Gao, X., Song, B., & Yang, D. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.11.059 · Cited by 26
Engineering Deep Representations for Modeling Aesthetic Perception
Chen, Y., Hu, Y., Zhang, L., Li, P., & Zhang, C. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2758350 · Cited by 28
Learning Deep Spatial Lung Features by 3D Convolutional Neural Network for Early Cancer Detection
Jin, T., Cui, H., Zeng, S., et al. (2017) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2017.8227454 · Cited by 26
Deep Bayesian network architecture for Big Data mining
Njah, H., Jamoussi, S., & Mahdi, W. (2018) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.4418 · Cited by 26
A Generative Adversarial Network for Tone Mapping HDR Images
Patel, V., Shah, P., & Raman, S. (2018) · Communications in computer and information science · doi:10.1007/978-981-13-0020-2_20 · Cited by 29
Assessment of Object Detection Using Deep Convolutional Neural Networks
Pathak, A. R., Pandey, M., Rautaray, S. S., et al. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-7245-1_45 · Cited by 26
Defect Detection of Photovoltaic Modules Based on Convolutional Neural Network
Sun, M., Lv, S., Zhao, X., Li, R., Zhang, W., & Zhang, X. (2018) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-73564-1_13 · Cited by 30
Awty‐Carroll, D., Brown, J. C., & Robson, P. R. H. (2018) · Plant Methods · doi:10.1186/s13007-018-0272-0 · Cited by 26
Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions
Zhang, R., Lin, L., Wang, G., Wang, M., & Zuo, W. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2799846 · Cited by 26
Weakly supervised learning with convolutional neural networks for power line localization
Lee, S. J., Yun, J. P., Choi, H., et al. (2017) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2017.8285410 · Cited by 26
Density Weighted Connectivity of Grass Pixels in image frames for biomass estimation
Zhang, L., Verma, B., Stockwell, D. R. B., et al. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.01.055 · Cited by 26
Alawad, M., Yoon, H., & Tourassi, G. D. (2018) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2018.8333408 · Cited by 26
A Unified Framework for Domain Adaptation Using Metric Learning on Manifolds
Mahadevan, S., Mishra, B., & Ghosh, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-10928-8_50 · Cited by 25
Co-design of deep neural nets and neural net accelerators for embedded vision applications
Kwon, K., Amid, A., Gholami, A., Wu, B., Asanović, K., & Keutzer, K. (2018) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/3195970.3199849 · Cited by 28
Scene Categorization Using Deeply Learned Gaze Shifting Kernel
Sun, X. N., Zhang, L., Wang, Z., Chang, J., Yao, Y., Li, P., & Zimmermann, R. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2820731 · Cited by 24
Hyperspectral Image Classification With Stacking Spectral Patches and Convolutional Neural Networks
Shu, L., McIsaac, K., & Osinski, G. R. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2829400 · Cited by 26
Wang, C., Elazab, A., Jia, F., Wu, J., & Hu, Q. (2018) · BioMedical Engineering OnLine · doi:10.1186/s12938-018-0496-2 · Cited by 26
How to Refine 3D Hand Pose Estimation from Unlabelled Depth Data?
Dibra, E., Wolf, T., Öztireli, C., & Groß, M. H. (2017) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00025 · Cited by 25
Where to Prune: Using LSTM to Guide End-to-end Pruning
Zhong, J., Ding, G., Guo, Y., Han, J., & Wang, B. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/445 · Cited by 28
lifeXplore at the Lifelog Search Challenge 2018
Münzer, B., Leibetseder, A., Kletz, S., Primus, M. J., & Schoeffmann, K. (2018) · ACM Workshop on the Lifelog Search Challenge (LSC) · doi:10.1145/3210539.3210541 · Cited by 28
Dey, S., Kalliatakis, G., Saha, S., Singh, A. K., Ehsan, S., & McDonald-Maier, K. D. (2018) · NASA/ESA Conference on Adaptive Hardware and Systems (AHS) · doi:10.1109/ahs.2018.8541406 · Cited by 26
Improving multiclass classification by deep networks using DAGSVM and Triplet Loss
Agarwal, N., Balasubramanian, V. N., & Jawahar, C. V. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.06.034 · Cited by 26
Cybernetic microbial detection system using transfer learning
Samuel, R. D. J., & Kanna, B. R. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6356-z · Cited by 26
Machine learning approach for homolog chromosome classification
Devaraj, S. (2018) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22287 · Cited by 25
Optimum Selection of DNN Model and Framework for Edge Inference
Velasco-Montero, D., Fernández‐Berni, J., Carmona‐Galán, R., & Rodriguez-Vazquez, A. (2018) · IEEE Access · doi:10.1109/access.2018.2869929 · Cited by 25
FitCNN: A cloud-assisted and low-cost framework for updating CNNs on IoT devices
Liu, D., Yang, C., Li, S., Chen, X., Ren, J., Liu, R., Duan, M., Tan, Y., & Liang, L. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.09.020 · Cited by 25
Vegetable Image Retrieval with Fine-tuning VGG Model and Image Hash
Yang, Z., Yue, J., Li, Z., et al. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.08.175 · Cited by 26
Objective Liver Fibrosis Estimation from Shear Wave Elastography
Brattain, L. J., Telfer, B. A., Dhyani, M., Grajo, J. R., & Samir, A. E. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8513011 · Cited by 28
Lee, S. L., Zare, M. R., & Müller, H. (2018) · IET Image Processing · doi:10.1049/iet-ipr.2018.5054 · Cited by 28
Ultrafast (milliseconds), multidimensional RF pulse design with deep learning
Vinding, M. S., Skyum, B., Sangill, R., & Lund, T. E. (2019) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27740 · Cited by 26
Opium Poppy Detection Using Deep Learning
Liu, X., Tian, Y., Yuan, C., et al. (2018) · Remote Sensing · doi:10.3390/rs10121886 · Cited by 26
Azizah, L. M., Umayah, S. F., & Fajar, F. (2018) · Semesta Teknika · doi:10.18196/st.212229 · Cited by 26
Recognition of Road Type and Quality for Advanced Driver Assistance Systems with Deep Learning
Tümen, V., Yıldırım, Ö., & Ergen, B. (2018) · Elektronika ir Elektrotechnika · doi:10.5755/j01.eie.24.6.22293 · Cited by 25
A Fast Algorithm for Convolutional Neural Networks Using Tile-based Fast Fourier Transforms
Lin, J., & Yao, Y. (2019) · Neural Processing Letters · doi:10.1007/s11063-019-09981-z · Cited by 24
GIF2Video: Color Dequantization and Temporal Interpolation of GIF Images
Wang, Y., Huang, H., Wang, C., He, T., Wang, J., & Hoai, M. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00151 · Cited by 23
Semi-supervised hotspot detection with self-paced multi-task learning
Chen, Y., Lin, Y., Gai, T., et al. (2019) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1145/3287624.3287685 · Cited by 26
Fang, S., Li, C.-C., Lu, W.-C., Xu, Z., & Chien, Y. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2892563 · Cited by 26
Metadata
- Authors
- Krizhevsky, A., Sutskever, I., & Hinton, G. E.
- Affiliations
- Google (United States) (US)
- University of Toronto (CA)
- Venue
- Communications of the ACM
- Published
- 2017-05-24
- Volume
- 60
- Issue
- 6
- Pages
- 84-90
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2163605009
- Cited by
- 112,155
- References
- 27
- Citations by year
- 2026: 2710 · 2025: 7710 · 2024: 10211 · 2023: 12267 · 2022: 12280 · 2021: 15497 · 2020: 14538 · 2019: 13976 · 2018: 9999 · 2017: 6661
- FWCI
- 5127.18
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- neural information processing systems
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- ImageNet, image classification, deep convolutional neural networks, dropout regularization, overfitting
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-11, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-11 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
