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 19,101–19,200 of 108,437
SSAP: Single-Shot Instance Segmentation With Affinity Pyramid
Gao, N., Shan, Y., Wang, Y., Zhao, X., & Huang, K. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2985420 · Cited by 55
Rate Control for Video-Based Point Cloud Compression
Li, L., Zhu Li, B., Liu, S. T., et al. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2989576 · Cited by 50
Machine learning for point counting and segmentation of arenite in thin section
Tang, D. G., Milliken, K. L., & Spikes, K. T. (2020) · Marine and Petroleum Geology · doi:10.1016/j.marpetgeo.2020.104518 · Cited by 53
Deep Attention and Multi-Scale Networks for Accurate Remote Sensing Image Segmentation
Qi, X., Li, K., Liu, P., Zhou, X., & Sun, M. (2020) · IEEE Access · doi:10.1109/access.2020.3015587 · Cited by 50
Wang, S., Zhou, J. P., Lei, T., et al. (2020) · Remote Sensing · doi:10.3390/rs12172691 · Cited by 51
Al-Sharman, M., Murdoch, D., Cao, D., Lv, C., Zweiri, Y., Rayside, D., & Melek, W. (2021) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2020.1003474 · Cited by 52
Deep understanding of shopper behaviours and interactions using RGB-D vision
Paolanti, M., Pietrini, R., Mancini, A., Frontoni, E., & Zingaretti, P. (2020) · Machine Vision and Applications · doi:10.1007/s00138-020-01118-w · Cited by 55
Cheng, H., Yu, H., Zeng, P., Osipov, E., Li, S., & Vyatkin, V. V. (2020) · Sensors · doi:10.3390/s20195659 · Cited by 53
Classification and Detection of Breathing Patterns with Wearable Sensors and Deep Learning
McClure, K., ERDREICH, B. H., Bates, J. H. T., McGinnis, R. S., Masquelin, A. H. P., & Wshah, S. (2020) · Sensors · doi:10.3390/s20226481 · Cited by 59
Li, J., Zhang, L., Johnson-Buck, A. E., & Walter, N. G. (2020) · Nature Communications · doi:10.1038/s41467-020-19673-1 · Cited by 49
Chen, C., Yan, J., Wang, L., Liang, D., & Zhang, W. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3044250 · Cited by 51
A Deep Neural Network-Based Feature Fusion for Bearing Fault Diagnosis
Hoang, D.-T., Tran, X.-T., Van, M., & Kang, H. (2021) · Sensors · doi:10.3390/s21010244 · Cited by 52
Inversion of magnetic data using deep neural networks
Hu, Z., Liu, S., Hu, X., Fu, L., Qu, J., Wang, H., & Chen, Q. (2021) · Physics of The Earth and Planetary Interiors · doi:10.1016/j.pepi.2021.106653 · Cited by 56
Fetal birthweight prediction with measured data by a temporal machine learning method
Tao, J., Yuan, Z., Sun, L., et al. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01388-y · Cited by 51
A Nighttime Vehicle Detection Method Based on YOLO v3
Miao, Y., Liu, F., Hou, T., Liu, L., & Liu, Y. (2020) · 2020 Chinese Automation Congress (CAC) · doi:10.1109/cac51589.2020.9326819 · Cited by 51
Image Augmentation for Deep Learning Based Lesion Classification from Skin Images
Göçeri, E. (2020) · IEEE International Conference on Image Processing, Applications and Systems (IPAS) · doi:10.1109/ipas50080.2020.9334937 · Cited by 76
Image Enhancement for Remote Photoplethysmography in a Low-Light Environment
Lin, X., Chen, W., Zhao, C., Wu, X., & Wang, J. (2020) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg47880.2020.00076 · Cited by 55
Image Classification Algorithm Based on Improved AlexNet
Li, S., Wang, L., Li, J., et al. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1813/1/012051 · Cited by 51
Chen, M., Jiang, H., Liao, W., & Zhao, T. (2022) · Information and Inference A Journal of the IMA · doi:10.1093/imaiai/iaac001 · Cited by 81
A Comparative Study of Window Size and Channel Arrangement on EEG-Emotion Recognition Using Deep CNN
Keelawat, P., Thammasan, N., Numao, M., & Kijsirikul, B. (2021) · Sensors · doi:10.3390/s21051678 · Cited by 56
A Review on Deep Learning in Minimally Invasive Surgery
Rivas-Blanco, I., Pérez-del-Pulgar, C. J., Garcia-Morales, I., & Muñoz, V. F. (2021) · IEEE Access · doi:10.1109/access.2021.3068852 · Cited by 54
Arithmetic-intensity-guided fault tolerance for neural network inference on GPUs
Kosaian, J., & Rashmi, K. V. (2021) · Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis · doi:10.1145/3458817.3476184 · Cited by 53
Bahgat, W. M., Balaha, H. M., AbdulAzeem, Y., & Badawy, M. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.555 · Cited by 50
Self-attention based Text Knowledge Mining for Text Detection
Wan, Q., Ji, H., & Shen, L. (2021) · 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/cvpr46437.2021.00592 · Cited by 51
PCB Defect Detection Using Deep Learning Methods
Wu, X., Ge, Y., Zhang, Q., et al. (2021) · IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd49262.2021.9437846 · Cited by 53
A Review on Convolutional Neural Network Encodings for Neuroevolution
Hakim, G. A. V., Mezura‐Montes, E., & Acosta‐Mesa, H. (2021) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2021.3088631 · Cited by 52
Lee, R., Venieris, S. I., & Lane, N. D. (2021) · ACM Computing Surveys · doi:10.1145/3469094 · Cited by 56
Shot Contrastive Self-Supervised Learning for Scene Boundary Detection
Chen, S., Nie, X., Fan, D., Zhang, D., Bhat, V., & Hamid, R. (2021) · 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/cvpr46437.2021.00967 · Cited by 62
Developing a new deep learning CNN model to detect and classify highway cracks
Elghaish, F., Talebi, S., Abdellatef, E., Matarneh, S. T., Hosseini, M. R., Wu, S., Mayouf, M., Hajirasouli, A., & Nguyen, T.-Q. (2021) · Journal of Engineering Design and Technology · doi:10.1108/jedt-04-2021-0192 · Cited by 55
Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models
Biswas, S., Chatterjee, S., Majee, A., Sen, S., Schwenker, F., & Sarkar, R. (2021) · Applied Sciences · doi:10.3390/app11157004 · Cited by 52
STN PLAD: A Dataset for Multi-Size Power Line Assets Detection in High-Resolution UAV Images
Vieira-e-Silva, A. L. B., Felix, H. D. C., Chaves, T., Simões, F., Teichrieb, V., Santos, M. M., Santiago, H. D. C., Sgotti, V., & Neto, H. B. D. T. L. (2021) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi54419.2021.00037 · Cited by 52
Palmprint-Palmvein Fusion Recognition Based on Deep Hashing Network
Wu, T., Leng, L., Khan, M. K., & Khan, F. A. (2021) · IEEE Access · doi:10.1109/access.2021.3112513 · Cited by 54
Student Behavior Recognition in Classroom using Deep Transfer Learning with VGG-16
Abdallah, T. B., Elleuch, I., & Guermazi, R. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.08.098 · Cited by 57
Han, R., Zhang, Q., Liu, C. H., Wang, G., Tang, J., & Chen, L. Y. (2021) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3447993.3483249 · Cited by 51
Yakkati, R. R., Yakkati, R. R., Tripathy, R. K., et al. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3128395 · Cited by 51
Bublin, M. (2021) · Sensors · doi:10.3390/s21227527 · Cited by 51
Çetinoğlu, Y. K., Koska, İ. Ö., Uluç, M. E., et al. (2021) · European Journal of Radiology · doi:10.1016/j.ejrad.2021.110050 · Cited by 51
The Development and Application of Machine Learning in Atmospheric Environment Studies
Zheng, L., Lin, R., Wang, X., & Chen, W. (2021) · Remote Sensing · doi:10.3390/rs13234839 · Cited by 55
Score-based mask edge improvement of Mask-RCNN for segmentation of fruit and vegetables
Hameed, K., Chai, D., & Rassau, A. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116205 · Cited by 50
Khozani, Z. S., Iranmehr, M., & Mohtar, W. H. M. W. (2022) · Geocarto International · doi:10.1080/10106049.2022.2032388 · Cited by 53
Zhang, G., Li, Y., Jiang, W., & Shu, L. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.01.067 · Cited by 51
Estimating Rainfall Intensity Using an Image-Based Deep Learning Model
Yin, H., Zheng, F., Duan, H., Savic, D. A., & Kapelan, Z. S. (2022) · Engineering · doi:10.1016/j.eng.2021.11.021 · Cited by 57
Bagherzadeh, S., Maghooli, K., Shalbaf, A., & Maghsoudi, A. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103544 · Cited by 53
Semantic segmentation and photogrammetry of crowdsourced images to monitor historic facades
Liu, Z., Brigham, R., Long, E. R., Wilson, L., Frost, A., Orr, S. A., & Grau‐Bové, J. (2022) · Heritage Science · doi:10.1186/s40494-022-00664-y · Cited by 50
Classification of COVID-19 from chest x-ray images using deep features and correlation coefficient
Kumar, R., Arora, R., Bansal, V., Sahayasheela, V. J., Buckchash, H., Imran, J., Narayanan, N., Pandian, G. N., & Raman, B. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12500-3 · Cited by 53
Qin, Z., Liu, Z., Guo, Q., & Zhu, P. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103828 · Cited by 56
Machine-Learning for Mapping and Monitoring Shallow Coral Reef Habitats
Burns, C., Bollard, B. A., & Narayanan, A. (2022) · Remote Sensing · doi:10.3390/rs14112666 · Cited by 62
[Retracted] An Automated Deep Learning Model for the Cerebellum Segmentation from Fetal Brain Images
Sreelakshmy, R., Titus, A., Sasirekha, N., Logashanmugam, E., Begam, R. B., Ramkumar, G., & Raju, R. (2022) · BioMed Research International · doi:10.1155/2022/8342767 · Cited by 51
RetractedKim, B. C., Kim, H., Moon, Y., Lee, G., & Mun, D. (2022) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwac056 · Cited by 59
SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability Detection
Zhang, L., Li, Y., Jin, T., Wang, W., Jin, Z., Zhao, C., Cai, Z., & Chen, H. (2022) · Sensors · doi:10.3390/s22124621 · Cited by 52
Robust convolutional neural networks against adversarial attacks on medical images
Shi, X., Peng, Y., Chen, Q., Keenan, T. D. L., Thavikulwat, A. T., Lee, S., Tang, Y., Chew, E. Y., Summers, R. M., & Lu, Z. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108923 · Cited by 52
mmSpy: Spying Phone Calls using mmWave Radars
Basak, S., & Gowda, M. (2022) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46214.2022.9833568 · Cited by 56
A Soft-Voting Ensemble Classifier for Detecting Patients Affected by COVID-19
Manconi, A., Giuliano Armano, B., Gnocchi, M., & Milanesi, L. (2022) · Applied Sciences · doi:10.3390/app12157554 · Cited by 54
A Hybrid ConvLSTM-Based Anomaly Detection Approach for Combating Energy Theft
Gao, H., Kuenzel, S., & Zhang, X. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3201569 · Cited by 56
Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm
Haseena, S., Saroja, S., Madavan, R., et al. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/1413597 · Cited by 51
Cai, W., Ning, X., Zhou, G., Bai, X. P., Jiang, Y., Li, W., & Qian, P. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3201056 · Cited by 53
Šušteršič, T., Ranković, V., Milovanovic, V., Kovačević, V., Rasulić, L., & Filipović, N. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3209585 · Cited by 52
He, Q., Sun, X., Diao, W., Yan, Z., Yin, D., & Fu, K. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.08.010 · Cited by 51
RetractedA Review of the Optimal Design of Neural Networks Based on FPGA
Wang, C., & Luo, Z. (2022) · Applied Sciences · doi:10.3390/app122110771 · Cited by 58
CoT-XNet: contextual transformer with Xception network for diabetic retinopathy grading
Zhao, S., Wu, Y., Tong, M., Yao, Y., Qian, W., & Qi, S. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac9fa0 · Cited by 51
A deep learning-based intrusion detection system for in-vehicle networks
Alqahtani, H., & Kumar, G. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.108447 · Cited by 51
Voon, W., Hum, Y. C., Tee, Y. K., Yap, W., Salim, M. I. M., Tan, T. S., Mokayed, H., & Lai, K. W. (2022) · Scientific Reports · doi:10.1038/s41598-022-21848-3 · Cited by 59
Tsolakis, I. A., Tsolakis, A. I., Elshebiny, T., Matthaios, S., & Palomo, J. M. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11226854 · Cited by 51
DrugAI: a multi-view deep learning model for predicting drug–target activating/inhibiting mechanisms
Zhang, S., Yang, K., Liu, Z., Lai, X., Yang, Z., Zeng, J., & Li, S. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac526 · Cited by 52
UAV Swarms in Smart Agriculture: Experiences and Opportunities
Qu, C., Boubin, J. G., Gafurov, D., Zhou, J., Aloysius, N. R., Nguyen, H., & Calyam, P. P. (2022) · IEEE International Conference on eScience · doi:10.1109/escience55777.2022.00029 · Cited by 64
One Loss for Quantization: Deep Hashing with Discrete Wasserstein Distributional Matching
Doan, K. D., Yang, P., & Li, P. (2022) · 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/cvpr52688.2022.00923 · Cited by 54
StrokeViT with AutoML for brain stroke classification
Raj, R., Mathew, J., Kannath, S. K., & Rajan, J. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105772 · Cited by 57
Kim, K., Jang, S., Park, J.-H., Lee, E., & Lee, S.-S. (2023) · Sensors · doi:10.3390/s23031185 · Cited by 61
Towards Online Domain Adaptive Object Detection
Vibashan, V. S., Oza, P., & Patel, V. M. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00055 · Cited by 56
Deep learning-based instantaneous cutting force modeling of three-axis CNC milling
Xie, J., Hu, P., Chen, J., Han, W., & Wang, R. (2023) · International Journal of Mechanical Sciences · doi:10.1016/j.ijmecsci.2023.108153 · Cited by 52
Foundations of machine learning for low-temperature plasmas: methods and case studies
Bonzanini, A. D., Shao, K., Graves, D. B., Hamaguchi, S., & Mesbah, A. (2023) · Plasma Sources Science and Technology · doi:10.1088/1361-6595/acb28c · Cited by 55
ResiDualGAN: Resize-Residual DualGAN for Cross-Domain Remote Sensing Images Semantic Segmentation
Zhao, Y., Guo, P., Sun, Z., Chen, X., & Gao, H. (2023) · Remote Sensing · doi:10.3390/rs15051428 · Cited by 53
Template-guided frequency attention and adaptive cross-entropy loss for UAV visual tracking
Xue, Y., Guodong, J. I. N., Tao, S. H. E. N., Tan, L., & Lianfeng, W. A. N. G. (2023) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2023.03.048 · Cited by 52
PM2.5 and O3 concentration estimation based on interpretable machine learning
Wang, S., Ren, Y., & Xia, B. (2023) · Atmospheric Pollution Research · doi:10.1016/j.apr.2023.101866 · Cited by 59
Machine learning-assisted structure annotation of natural products based on MS and NMR data
Hu, G., & Qiu, M. (2023) · Natural Product Reports · doi:10.1039/d3np00025g · Cited by 55
Integration of Deep Learning for Automatic Recognition of 2D Engineering Drawings
Lin, Y.-H., Ting, Y.-H., Huang, Y.-C., Cheng, K.-L., & Jong, W. (2023) · Machines · doi:10.3390/machines11080802 · Cited by 58
Skin Lesion Synthesis and Classification Using an Improved DCGAN Classifier
Behara, K., Bhero, E., & Agee, J. T. (2023) · Diagnostics · doi:10.3390/diagnostics13162635 · Cited by 50
Yang, Y., Hossain, M. Z., Stone, E. A., & Rahman, S. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109966 · Cited by 57
Shilpa, S., Kashyap, G., & Sunoj, R. B. (2023) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.3c04779 · Cited by 57
ALADA: A lite automatic data augmentation framework for industrial defect detection
Wang, Y., Chung, S., Khan, W. A., Wang, T., & Xu, D. J. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102205 · Cited by 52
Lung Cancer Classification in Histopathology Images Using Multiresolution Efficient Nets
Anjum, S., Ahmed, I., Asif, M., Aljuaid, H., Alturise, F., Ghadi, Y. Y., & Elhabob, R. (2023) · Computational Intelligence and Neuroscience · doi:10.1155/2023/7282944 · Cited by 59
Nawaz, S. A., Li, J., Shoukat, M. U., et al. (2023) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2023.108985 · Cited by 51
Quasar, S. R., Sharma, R., Mittal, A., Sharma, M., Agarwal, D., & Díez, I. D. L. T. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17616-8 · Cited by 58
Koide‐Majima, N., Nishimoto, S., & Majima, K. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.11.024 · Cited by 68
Lu, Z., Ozek, B., & Kamarthi, S. V. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1294577 · Cited by 65
Safdar, M. F., Nowak, R. M., & Pałka, P. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107908 · Cited by 61
Deep Model Intellectual Property Protection With Compression-Resistant Model Watermarking
Nie, H., Lu, S., Wu, J., & Jian-xin, Z. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3351116 · Cited by 53
Railway obstacle intrusion warning mechanism integrating YOLO-based detection and risk assessment
Zhang, Z., Chen, P., Huang, Y., Dai, L., Xu, F., & Hu, H. (2024) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2024.100571 · Cited by 52
SparseSwin: Swin transformer with sparse transformer block
Pinasthika, K., Laksono, B. S. P., Irsal, R. B. P., Shabiyya, S. H., & Yudistira, N. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127433 · Cited by 55
DGA-GNN: Dynamic Grouping Aggregation GNN for Fraud Detection
Duan, M., Zheng, T., Gao, Y., Wang, G., Feng, Z., & Wang, X. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i10.29067 · Cited by 60
Nie, Y., Zelikman, E., Scott, A., Paletta, Q., & Brandt, A. R. (2024) · Advances in Applied Energy · doi:10.1016/j.adapen.2024.100172 · Cited by 66
Deep Learning-Assisted Spectrum–Structure Correlation: State-of-the-Art and Perspectives
Lu, X., Wu, H.-P., Ma, H., Li, H., Li, J. Z., Liu, Y.-T., Pan, Z.-Y., Xie, Y., Wang, L., Ren, B. W., et al. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.4c01639 · Cited by 56
Correlations of Cross-Entropy Loss in Machine Learning
Connor, R., Dearle, A., Claydon, B., & Vadicamo, L. (2024) · Entropy · doi:10.3390/e26060491 · Cited by 56
Hou, Y., Li, T., Wang, J., Ma, J., & Chen, Z. (2024) · Measurement · doi:10.1016/j.measurement.2024.115068 · Cited by 52
A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era
Ren, Z. X., Chang, Y., Nguyên, T. T., Tan, Y., Qian, K., & Schuller, B. W. (2024) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2024.3401309 · Cited by 74
Pre-sensor computing with compact multilayer optical neural network
Zheng, H., Shi, W., Wu, S., Wang, Y., Yang, S., & Chen, H. (2024) · Science Advances · doi:10.1126/sciadv.ado8516 · Cited by 56
High-resolution sensors and deep learning models for tree resource monitoring
Brandt, M., Chave, J., Li, S., Fensholt, R., Ciais, P., Wigneron, J., Gieseke, F. C., Saatchi, S. S., Tucker, C. J., & Igel, C. (2024) · Nature Reviews Electrical Engineering · doi:10.1038/s44287-024-00116-8 · Cited by 60
Ougahi, J. H., & Rowan, J. S. (2025) · Scientific Reports · doi:10.1038/s41598-025-87187-1 · Cited by 61
Deep learning for surface crack detection in civil engineering: A comprehensive review
Zhuang, H., Cheng, Y., Zhou, M., & Yang, Z. (2025) · Measurement · doi:10.1016/j.measurement.2025.116908 · Cited by 63
Li, L., & Duan, L. (2025) · International Journal of Production Research · doi:10.1080/00207543.2025.2462657 · Cited by 71
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
