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.
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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
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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)
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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)
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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
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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
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In the paper
“So these data augmentation schemes are, in effect, computationally free.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
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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
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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
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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
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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
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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.
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- Probably not a substantial claim. 26
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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
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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.
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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
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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.
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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.
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In the paper
“so we used several effective techniques for preventing overfitting”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
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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.
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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
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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.
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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.
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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
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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
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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.
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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.
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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.
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- 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
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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.
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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
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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.
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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
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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
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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
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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
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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).
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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
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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
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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
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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
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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
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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
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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
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In the paper
“which is considerably better than the previous state-of-the-art”
§ ImageNet Classification with Deep Convolutional Neural Networks > Abstract
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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.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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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
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Traffic Sign Detection using YOLOv8 Implementation, Optimization, and Performance Analysis
Appavu, N. (2025) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc65114.2025.11212304 · Cited by 1
“Strong object detection algorithms with remarkable accuracy and real-time performance have been developed thanks to recent developments in visual processing and deep learning [6].”
Fathimal, P. M., Krishna, T. S. A., Akshaya, R., et al. (2025) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc65114.2025.11212299 · Cited by 1
Geometry-aware lightweight convolutional network for efficient molecular property prediction
Zhang, H., Zhou, G., Tang, M., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-22049-4 · Cited by 1
“Early representative models such as AlexNet 35 and Inception v1 36 employed large convolution kernels of 11×11 and 7×7, respectively, to enhance the perception of local spatial features.”
Rethink the Application of Pyramid Structure Transformer Networks in Semantic Segmentation
Xu, Q., Wang, H., & Chen, F. (2025) · doi:10.1145/3766918.3766967 · Cited by 1
“However, with the advent of deep learning [7], Fully Convolutional Networks (FCN) emerged as a seminal work in the realm of semantic segmentation, paving the way for subsequent innovations.”
Fast and Energy-Efficient 2D Convolution: Inspection-Based Methods for Hardware Acceleration
Silva, T. O. D., & Moraes, F. (2025) · Symposium on Integrated Circuits and Systems Design · doi:10.1109/sbcci66862.2025.11218694 · Cited by 1
“Their widespread success stems from the ability to automatically learn salient image features, outperforming traditional approaches on numerous benchmarks [1, 2].”
Wang, F., Xie, X., Xiong, Y., et al. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3622055 · Cited by 1
Local Vectorized High Definition Map Construction for Autonomous Driving: A Comprehensive Review
Zhang, Y., Qian, Y., Meng, C., Zhang, R., Yi, H., Wang, C., & Yang, M. Z. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3623833 · Cited by 1
Xu, Y., Liu, Q., Xu, J., & Chen, S. (2025) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2025.10.116 · Cited by 2
Guan, J., & Wang, Y. (2025) · Journal of Mechanical Science and Technology · doi:10.1007/s12206-025-1012-9 · Cited by 3
An Enhanced Deep Learning Approach for Writer Identification and Verification Using Corner Detection
Khalaif, M. Z., & Younis, M. T. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-07244-3_14 · Cited by 1
Panjwani, S., Wei, H., & Mason, J. (2025) · Biotechnology Progress · doi:10.1002/btpr.70089 · Cited by 3
Sabahno, H., & Khodadad, D. (2025) · Scientific Reports · doi:10.1038/s41598-025-25489-0
Uncertainty Quantification for Semi-Supervised Object Detection in Remote Sensing Images
Yang, X., Li, P., Zhou, Q., Wang, N., & Gao, X. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3629033 · Cited by 2
Automatic Chest X-ray Image Caption for Report Generation
Li, J., & Zhang, Y. (2025) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1145/3721201.3725429 · Cited by 2
“The contributions of our paper are three-fold: For the image feature extraction task, deep learning models such as AlexNet [7], ResNet50[8], and YOLO [9] have been feature models.”
Chung, C., Yang, C.-W., & Chang, H.-M. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-16941-6 · Cited by 2
Matthews, S. C., Greco, A., Rastelli, C., & Nia, A. B. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1448043 · Cited by 1
A Visual Benchmark for Autonomous Driving in Open-Pit Mines
Li, Y., Li, L., Bing, Z., Sun, L., Knoll, A., Wang, F., & Chen, L. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3632357 · Cited by 1
An accurate pixel-Level explainable approach for CNNs and its application
Zhang, H., Wang, J., Wang, Z., et al. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108289 · Cited by 1
Transformers Outperform CNNs: A Large-Scale Benchmark for AI-Powered Skin Disease Diagnosis
Elhadidy, N. A., Salama, N. Y., ELselmy, F. E., et al. (2025) · Novel Intelligent and Leading Emerging Sciences Conference (NILES) · doi:10.1109/niles68063.2025.11232350 · Cited by 1
“Architectures like ResNet and EfficientNet capture spatial features well [3], [4], but often struggle with class imbalance and lack global context.”
HCNN-CasForest6: A Hybrid Deep Learning and Ensemble Framework for Fault Diagnosis
Hu, Z., Ding, W., & Ran, R. (2025) · doi:10.1109/ijcnn64981.2025.11229424 · Cited by 1
BATR-FST: Bi-Level Adaptive Token Refinement for Few-Shot Transformers
Al‐Habib, M. I., Zhang, Z., & Noman, A. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227827 · Cited by 1
EOOD: Entropy-based Out-of-distribution Detection
Yang, G., Hou, C., Peng, W., Fang, X., Nie, Y., Zhu, P., & Tang, K. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228668 · Cited by 5
Research on Dynamic Curriculum Learning in Mongolian-Chinese Neural Machine Translation
Hu, C., Si, Q., & Wang, S. (2025) · doi:10.1109/ijcnn64981.2025.11228447 · Cited by 1
“CL has found widespread application across domains including computer vision and natural language processing [15], where it has proven effective in enhancing model generalization and convergence rates.”
A Comprehensive Survey on Data Distillation: Techniques, Frameworks, and Future Directions
Razi, Q., Singh, S., Priyadarshini, R., Hassija, V., & Chalapathi, G. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3633710 · Cited by 1
Unsupervised Online Learning for Substation Equipment Defect Identification Based on SoftHebb
Zhang, Z., Zhang, G., Zhang, P., Chen, J., Liu, X., Peng, G., & Zhou, F. (2025) · Energy internet. · doi:10.1049/ein2.70012 · Cited by 2
“Further research by the authors in study [11] explored the integration of Hebbian learning with BP networks for image classification tasks, achieving impressive results on the large‐scale classification dataset, ImageNet [4].”
Deep Learning-Based Inversion Analysis of Interlayer Contact Parameters in Pavement Structures
Han, Z., Ma, M., Yan, Z., et al. (2026) · Transportation Infrastructure Geotechnology · doi:10.1007/s40515-026-00860-4 · Cited by 1
“Currently, the most widely used CNN models mainly include four types: LeNet-5 network (Krizhevsky et al. 2017), AlexNet network (Chen et al. 2018 ), Inception network (Meena et al. 2023), and residual network (Baik et al. 2025 ).”
Multi-task monitoring of multi-scale wheat water status based on UAV multi-source data
Ni, M., Li, G., Zhou, B., Chen, H., Liu, Y., Zhao, X., Zhang, Y., Zhang, M., & Wang, M. (2025) · International Journal of Remote Sensing · doi:10.1080/01431161.2025.2588018 · Cited by 1
“Convolutional neural network (CNN) (Krizhevsky, Sutskever, and Hinton 2017), owing to local connectivity and weight-sharing, specialize in extracting hierarchical spatial patterns.”
InfluentialPatel, D. D., Sisodiya, P. P., Patel, O. M., et al. (2025) · doi:10.1109/aists66100.2025.11233175 · Cited by 1
“The resized images were then normalized to map features onto a similar scale (0, 1) [15].”
Hard Negative Sampling in Hyperedge Prediction
Deng, Z., Zhou, T., & Bi, Y. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3634750 · Cited by 1
Steganography via Neural Network Parameter Initialization With High Fidelity and Imperceptibility
Wang, N., Xu, C., Cao, F., Huang, L., Wang, W., & Qin, C. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3634666 · Cited by 1
Multimodal Deep Feature Fusion Network-Assisted Optimization of Microstrip Antenna Geometry Design
Gao, C., Xiao, S., Lei, B., Tian, Y., Zhang, X., & Li, H. (2025) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2025.3630869 · Cited by 2
Yaman, O. (2025) · Drones · doi:10.3390/drones9110807 · Cited by 4
“Convolutional Neural Networks (CNNs), although originally designed for image recognition tasks [23], have been successfully adapted for time-series classification [24,25] due to their ability to extract local temporal features through one-dimensional convolutions.”
Chen, H., Tang, R., Hong, M., Zhao, J., Lu, D., Luan, X., Zheng, G., & Zhang, W. (2025) · Chinese Journal of Natural Medicines · doi:10.1016/s1875-5364(25)60903-4 · Cited by 2
An RGBD fused real time defect detection and size estimation method for sewer robots
Shen, W., Zhang, J., Wei-ming, J., & Song, A. (2025) · Measurement · doi:10.1016/j.measurement.2025.119694 · Cited by 1
Transformer architectures for computer vision: A comprehensive review and future research directions
Ugile, T., & Uke, N. (2025) · EPJ Web of Conferences · doi:10.1051/epjconf/202534101023 · Cited by 2
Spikoder: Dual‐Mode Graphene Neuron Circuit for Hardware Intelligence
Mohanan, K. U., Sattari‐Esfahlan, S. M., Cho, E., Kymissis, I., & Kim, C. (2025) · Advanced Intelligent Systems · doi:10.1002/aisy.202500327 · Cited by 1
Şencan, C., Erkmen, A. M., & Ertuğrul, A. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130476 · Cited by 1
KFGOD: A Fine-Grained Object Detection Dataset in KOMPSAT Satellite Imagery
Lee, D., Hong, J. H., Seo, H. W., & Oh, H. (2025) · Remote Sensing · doi:10.3390/rs17223774 · Cited by 1
“While deep learning advancements have achieved remarkable performance in object detection for natural images [11–16], satellite images exhibit fundamental differences from them in terms of imaging environments and object characteristics, which limits the direct application of existing techniques…”
Huang, R., Yang, B., Wu, S., Li, Z., Doyle, P., Zhang, X., Chen, X., & Liu, Y. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112700 · Cited by 1
A Novel Road Crack Detection Method Based on the YOLO Algorithm
Fan, L., Xia, Q., & Zou, J. (2025) · Applied Sciences · doi:10.3390/app152312354 · Cited by 1
He, C., Zhou, X., Zhang, J., Luo, J., Chen, Y., Liu, X., Ma, S., & Sun, J. (2025) · IET Cyber-Systems and Robotics · doi:10.1049/csy2.70035 · Cited by 1
Computationally Probing the Role of Time‐Limited Neuronal Plasticity in Early Visual Development
Vogelsang, M., Vogelsang, L., & Sinha, P. (2025) · Developmental Science · doi:10.1111/desc.70099 · Cited by 1
Zhang, J., Wang, X., Zhang, Y., et al. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103995 · Cited by 1
Geoscientific Input Feature Selection for CNN-Driven Mineral Prospectivity Mapping
Kimiaghalam, A., Noh, K., & Swidinsky, A. (2025) · Minerals · doi:10.3390/min15121237 · Cited by 1
“A successful and widely implemented CNN architecture is AlexNet [8], which accepts images in RGB format, as a collection of three separate color input layers.”
Graph-FEM/ML Framework for Inverse Load Identification in Thick-Walled Hyperelastic Pressure Vessels
Firouzi, N., Hafez, R. M., Salloomi, K. N., Abdelkawy, M. A. A., & Hussain, R. R. (2025) · Symmetry · doi:10.3390/sym17122021 · Cited by 4
Lim, B., Zhu, K., Zorcic, K., et al. (2025) · American Journal of Nephrology · doi:10.1159/000549256 · Cited by 1
An Online Learning Framework for Fault Diagnosis of Rolling Bearings Under Distribution Shifts
Li, W., Wang, Y., Li, J., et al. (2025) · Mathematics · doi:10.3390/math13233763 · Cited by 1
Künstliche Intelligenz in der Frakturdiagnostik
Endler, C., Luetkens, J. A., & Nowak, S. (2025) · Die Unfallchirurgie · doi:10.1007/s00113-025-01653-z · Cited by 1
Li, W., Wang, J., & Luo, Y. (2025) · Applied Spatial Analysis and Policy · doi:10.1007/s12061-025-09769-w · Cited by 1
“…classification methods classify each pixel in the image, with commonly used algorithms including the support vector machine (SVM), Random Forest (RF) model, and various deep learning models (Breiman, 2001; Krizhevsky et al., 2017; Han et al., 2023; Jordan & Mitchell, 2015; Maxwell et al., 2018).”
Murao, A., Hara, K., Oyama, S., Ogura, A., Kishimoto, Y., Hatanaka, M., Fujita, N., Sato, M., Aiba, I., Kato, K., et al. (2025) · Annals of Nuclear Medicine · doi:10.1007/s12149-025-02132-6 · Cited by 1
“As pre-trained CNN architectures, we used AlexNet [ 19], GoogLeNet [20], ResNet-50 [21], SqueezeNet [22], Shuf-fleNet [ 23], and MobileNet-v2 [24].”
Optimal Utility Bounds for Differentially Private Gradient Descent in Three-Layer Neural Networks
Wang, P., Lei, Y., Kloft, M., et al. (2025) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa65442.2025.11247980 · Cited by 1
“Deep neural networks (DNNs) have achieved remarkable success in a wide range of applications, including image recognition [1], speech recognition [2], machine translation [3], and reinforcement learning [4].”
Privacy‐Preserving Hierarchical Federated Learning With Front‐Loaded Differential Privacy Mechanism
Ratnayake, H., Chen, L., & Ding, X. (2025) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.70411 · Cited by 1
“Conversely,fortheCIFAR-10dataset,alightweightCNNinspired by the AlexNet architecture [45] was implemented and tailored to the federated learning context.”
Fu, M., Wang, H., Jian, H., & Zhang, Y. (2025) · Journal of Environmental Management · doi:10.1016/j.jenvman.2025.128031 · Cited by 1
Gastric Neoplasm Detection at Contrast-enhanced CT with Deep Learning
Chen, X., Xia, Y., Yao, L., Li, S., Liang, Y., Zheng, Z., Yuan, M., Yao, J., Zhang, R., Tu, W., et al. (2025) · Radiology Artificial Intelligence · doi:10.1148/ryai.250145 · Cited by 1
Based on Tensor Core Sparse Kernels Accelerating Deep Neural Networks
Lv, S., Liu, D., Yang, L. T., Peng, X. T., Zhao, R., Yang, Z., & Feng, J. (2025) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2025.3637268 · Cited by 1
UniSparTa: A Unified Sparse Tensor Program Tuning Framework
Wang, Z. M., Gong, L., Qu, X., Tang, C. C., Lou, W., Wang, T., Cheng, Q., Chen, X., Wang, C., & Zhou, X. (2025) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2025.3636442 · Cited by 1
“For cross-data transfer, sparse tensors are modeled as images, which enables us to directly adopt data augmentation techniques from CV to improve robustness against variations in sparsity patterns [38], [39].”
Ding, N., Li, M., Zhou, C., & Tie, R. (2025) · Evolving Systems · doi:10.1007/s12530-025-09771-0 · Cited by 1
“Initially, Convolutional Neural Net - works (CNNs) (Krizhevsky et al. 2017 ) were applied to time series data processing.”
Yan, Y., Feng, C.-M., Ye, M., Zuo, W., Li, P., Goh, R. S. M., Zhu, L., & Chen, C. L. P. (2025) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2025.3632546 · Cited by 1
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Spatial Graph Attentional Network Based Place Recognition with Visual Mamba Embedding
Li, K., Ou, Y., Cai, H., Ning, J., & Qi, M. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247468 · Cited by 2
“We initialize the model with a CCT backbone [47] pre-trained on ImageNet [48] for feature extraction, followed by our VME module for global retrieval.”
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“Consequently, video classification has emerged as a critical tool, simplifying the identification of relevant content by organizing it into meaningful categories [3, 4].”
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
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