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
29 No basis given · 22 Own result · 19 Own interpretation · 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
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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
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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
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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
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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
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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
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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
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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
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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
plaindirect- 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
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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.
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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
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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
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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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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).
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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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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
“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
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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
plaindirect- 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
plaindirect- 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
plaindirect- 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
plaindirect- 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
plaindirect- 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.
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
“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
hedgeddirect- Asserted: passes — The authors put it forward as true.
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- 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
“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 measuredThe 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
“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
“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]
In 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
“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]
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]
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]
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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Going deeper with convolutions
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Cited by
Sorted by citations
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Iterative Explainability for Weakly Supervised Segmentation in Medical PE Detection
Condrea, F., Rapaka, S., & Leordeanu, M. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-7425-4_39 · Cited by 2
AI-Assisted Solar-Powered Military Surveillance Drones
Selvaraj, K. (2026) · IGI Global eBooks · doi:10.4018/979-8-3693-6487-1.ch006
Constella: A Novel Framework for Cost-Efficient Distributed AI Inference in LEO Space Data Centers
Stanisic, A., Gravara, M., Herrera, J. L., & Nastić, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-35251-4_24
“All models are analyzed using a fixed input tensor shape (3 , 224 , 224) , representing the standard input for the considered model architectures [13].”
A Novel Graph Convolutional Network-Based Method for Cervical Cytology Image Classification
Liu, Z., Qian, D., Zhu, H., & Niu, Y. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-7425-4_32
Research on Wheat Drought Stress Recognition Based on Improved EfficientNet-B0
Yao, J., Wang, M., Li, L., Xue, X., & Sun, J. (2026) · Agronomy · doi:10.3390/agronomy16161565
Multi‐Model Ensembles in Ecosystem Modeling: Challenges and Best Practices for Decision‐Making
Guan, K., Qin, R., Ma, Z., Zhao, L., Liu, L., Wang, S., Keenan, T. F., Jia, M., Sihi, D., Potash, E., et al. (2026) · Global Change Biology · doi:10.1111/gcb.70987 · Cited by 1
“…learning area: for the ImageNet annual competition in 2012, a convolutional neural network, AlexNet, achieved a shockingly better performance on the benchmark data set of more than 7.3 percentage points lower in a top-5 error than the rest of the best models at that time (Krizhevsky et al. 2017).”
InfluentialParuchuru, T., Potluri, H. S., Perni, V. R., et al. (2026) · International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) · doi:10.1109/icdcece68961.2026.11645987
“Several pretrained deep learning architectures are evaluated in the proposed framework, including VGG16, ResNet152V2, ResNet50V2, Xception, InceptionV3, MobileNetV3Large, and MobileNetV3Small [9], [18]–[20].”
On‐Chip Photonic Neural Network Architectures
Hong, S., Neşeli, B., Cho, S., Lee, S., Kim, Y., Choi, S. K., Park, H., & Kurt, H. (2026) · Advanced Optical Materials · doi:10.1002/adom.71567
Sterling, A. Y. J. (2025) · 嶺南科學工業學報 · doi:10.67298/paper/720001
Second-order season-trend GRU for long time series forecasting
Zhang, L., Li, Y., & Chen, R. (2026) · Evolving Systems · doi:10.1007/s12530-026-09855-5
“CNN (Krizhevsky et al. 2017) models employ convolutional kernels to extract temporal features from time series, which significantly enhances its accuracy in long-term time series prediction tasks.”
Analysis of MLP, CNN, and Transfer Learning Using VGG-16 for CIFAR-10 Dataset
Mahmood, S., Gohar, M., Alvi, A. W., et al. (2024) · International Journal of Innovations in Science and Technology · doi:10.33411/ijist/1086
Deng, Z., Ding, L., Tao, S., & Zhang, H. (2026) · Cogent Arts and Humanities · doi:10.1080/23311983.2026.2707050
Feng, X., Zhang, J., Chen, S., Dong, Z., Chen, Z., Lin, Z., & Qi, L. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7289607
GNN-RL: Multi-objective HPC scheduling with graph neural networks and reinforcement learning
Adimora, K. C., & Sun, H. (2026) · Future Generation Computer Systems · doi:10.1016/j.future.2026.108763
Skin Disease Prediction Software
Tripathi, A., Sharma, A., Aashi, et al. (2026) · International Journal of Technology & Emerging Research · doi:10.64823/ijter.2604036
Prediction of emulsification drop size distributions using artificial neural networks
Solanki, F. S., Hanandhito, J. K., Rogers, A. W., et al. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.112487
Gated Multi-Backbone Early Fusion of CNN and Swin Transformer for Cattle Disease Classification
Tiwari, A., Saxena, A. K., & Sahu, S. (2026) · IEEE Guwahati Subsection Conference (GCON) · doi:10.1109/gcon69192.2026.11648697
“However, using just a single model is often enough to capture both the fine-grained details and the broader structural patterns found in complex images [5-7].”
Efficient Approximate Convolution with Tunable Error Tolerance for CNNs
Shashidhar, V., Kumari, A., & Paily, R. P. (2026) · IEEE Guwahati Subsection Conference (GCON) · doi:10.1109/gcon69192.2026.11648726
“…negligible fraction of the model’s parameters [1] while Fully Connected layers dominate the model’s memory footprint due to dense interconnections [2], however, modern architectures such as ResNet and GoogLeNet mitigate the heavy FC layers, shifting the parameter distribution more toward…”
Wang, D., Jin, Y., Luo, W., et al. (2026) · Sensors · doi:10.3390/s26165196
“However, in complex environments with severe image distortion, methods based solely on depth image pixel values exhibit low robustness [46].”
DNA-encoded libraries through a machine learning lens: challenges and opportunities
Bahia, M. S., Jiang, Y., Gonse, A., Batey, R. A., & Koziarski, M. (2026) · Digital Discovery · doi:10.1039/d6dd00117c
Patil, C. H., Mali, S. M., & Nagane, A. S. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-25490-0_22
A Residual Attention Framework for Corn Leaf Disease Diagnosis Under Resource Constraints
Srinivasan, P., & Kokil, P. (2026) · IEEE Guwahati Subsection Conference (GCON) · doi:10.1109/gcon69192.2026.11648343
“DL architectures, namely AlexNet [4], ResNet [5], and MobileNet [6], among others, based on transfer learning, have achieved good performance in disease classification but incur higher computational costs, which will impede the implementation of the DL networks in resource-limited scenarios, such…”
A Multi-Modal Fusion Deep Learning Method for Radar Target Recognition
Cai, X., Meng, C., Qu, S., & Li, H. (2026) · International Conference on Computer Vision, Image and Deep Learning (CVIDL) · doi:10.1109/cvidl70130.2026.11637915
“Since then, classic CNN structures such as AlexNet [11] , VGGNet [12] , and ResNet [13] have been proposed successively, promoting the rapid development and application of CNNs. Relying on the advantages of feature extraction of convolutional neural networks, this paper constructs a single-modal…”
Ishii, R., Edwards, R. A., Gelbach, P. E., & Alexander, J. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-02172-y
Composite multi-exit contrastive learning for causal disentangling of deep representations
Wang, B., Dornaika, F., & Charafeddine, J. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.115967
Transfer Learning for Fine-Grained Image Classification Under Controlled Fine-Tuning Settings
Li, S. (2026) · International Conference on Computer Vision, Image and Deep Learning (CVIDL) · doi:10.1109/cvidl70130.2026.11637858
“With the availability of large-scale labeled benchmarks such as ImageNet [1] and the success of deep convolutional networks, deep learning became a dominant approach to visual recognition, as demonstrated by early large-scale CNN systems such as AlexNet [5].”
Shen, Y., Li, Z., Lu, P., et al. (2026) · International Conference on Computer Vision, Image and Deep Learning (CVIDL) · doi:10.1109/cvidl70130.2026.11637500
“Early efforts relied on classical convolutional networks pioneered by AlexNet [1], advanced by deeper architectures such as ResNet [2] and DenseNet [3].”
Godines-Torres, O. B., Pintor-Estrada, A. A., Vega-Flores, M. Y., et al. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1016391
On the comparative performance of neural networks for Kerr black hole
Rehman, K. U., Shatanawi, W., & Alharbi, W. G. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-12396-2
A Patient-Level Cross-Validated Deep Learning-Based Multiclass Nail Disease Classification
Hugar, G., Yellapur, R., Surasura, P., Kundgol, S., Kanavi, V., & Kyatanavar, S. (2026) · International Conference on Emerging Trends in Information, Communication & Systems (ICETICS) · doi:10.1109/icetics69505.2026.11651091
“Convolutional neural networks (CNNs) have claimed their superiority when it comes to representation learning and have even conquered very large benchmarks in computer vision, which is just one of the reasons why they are getting more and more support in the medical sector [1], [20], [21].”
DINOv3-Based Cross-Domain Transfer for UAV Thermal Photovoltaic Fault Recognition
Wang, H., Shen, Z.-M., Hu, K., et al. (2026) · 2026 IEEE 2nd International Conference on Electronics, Energy Systems and Power Engineering (EESPE) · doi:10.1109/eespe68405.2026.11649036
“Several studies have utilized deep CNN architectures such as AlexNet[5] and VGGNet[6] to detect faults from UAV-captured images of photovoltaic modules.”
Rajalakshmi, K., Varsha, J. M., Saraswathi, S., et al. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-28094-7_72
Zhong, D. (2026) · IEEE International Conference on Electronics, Energy Systems and Power Engineering (EESPE) · doi:10.1109/eespe68405.2026.11648988
“…model are as follows: AlexNet model: This model consists of 5 convolutional layers and 3 fully connected layers, with a Dropout rate set to 0.5 [9] EfficientNet-B2 model: This model achieves a balance between computational efficiency and model performance through a composite scaling mechanism,…”
InfluentialLi, T., & Zhang, X. (2026) · International Communications in Heat and Mass Transfer · doi:10.1016/j.icheatmasstransfer.2026.112341
Huang, J., Fan, K., Zeng, Z., et al. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae96fa
“…a lightweight convolutional neural network model proposed by Google in 2018, specifically designed for mobile devices and embedded systems AlexNet [27], proposed by Alex Krizhevsky in 2012, consists of eight layers, including five convolutional layers and three fully connected (FC) layers, with a…”
Fe3+-Mediated Interfacial Polyphenol Coatings on Hair Fibers Using an Aronia melanocarpa Extract
Yan, S., Gu, Y., Kong, Y., Xiang, Y., Yang, B., Su, X., Sheng, L., & Song, K. (2026) · Plants · doi:10.3390/plants15162500
Residual Conditional Expert Block for Adaptive Weld Defect Recognition in Radiographic NDT
Reddy, J. B., Verma, S., & Prasad, M. S. (2026) · doi:10.1109/neuront71829.2026.11651395
Lara, F., Zambrano, A., Lara-Cueva, R., & Grijalva, F. (2026) · IEEE Access · doi:10.1109/access.2026.3725191
Vij, S., Mohmmad, S., Nasra, P., Sharma, S., & Sharma, S. P. (2026) · International Conference on Emerging Trends in Information, Communication & Systems (ICETICS) · doi:10.1109/icetics69505.2026.11651330
“The development of deep CNNs can be attributed to the work of Krizhevsky et al. [6] in their AlexNet model for image classification.”
GLNet: global-to-local aware hybrid framework for medical image segmentation
Sun, D., Liu, L., Zhao, X., et al. (2026) · Multimedia Systems · doi:10.1007/s00530-026-02603-4
“However, with the rapid development of deep learning techniques [ 18], manual feature extraction is no longer necessary for medical image segmentation.”
Enhancing Pest Detection in Agriculture: A Multi-Scale Feature Fusion Approach with YOLOv3
Zhang, H., Liu, X., Wang, C., Tang, J., Shen, C., & Liu, J. (2026) · Agronomy · doi:10.3390/agronomy16161591
A Lightweight Multi-Perspective Fusion Framework for Stock Price Forecasting
Bao, Z., Yang, Y., Li, X., & Wang, S. (2026) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001426590391
Ha, P. N., Tran, G. S., & Doucet, A. (2026) · International Conference on Intelligent Information Technology (ICIIT) · doi:10.1145/3805862.3805947
“Early CNN-based models such as AlexNet and VGGNet achieved reasonable classification performance but often relied heavily on data augmentation and regularization to mitigate overfitting [7], [8].”
Sayed, M., & Mohamed, H. (2026) · Journal of Data Analysis and Information Processing · doi:10.4236/jdaip.2026.143018
Using Binary Neural Networks for fast Image Classification in Resource-Constrained Environments
Maleev, O. G. (2026) · 2026 VII International Conference on Neural Networks and Neurotechnologies (NeuroNT) · doi:10.1109/neuront71829.2026.11650779
“For the experiments, we constructed a 15-class vehicle dataset based on ImageNet [12].”
Yan, X., Shi, Z., Sun, S., et al. (2026) · Drones · doi:10.3390/drones10080632
“Workman and Jacobs [19] pioneered the introduction of the pre-trained AlexNet [20] model for feature mining, which not only confirmed the critical role of high-level semantic information but also demonstrated its superiority over traditional hand-crafted descriptors.”
Wang, P., Zhao, H., Liu, Z., Tao, Y., Miao, Q., Liu, S., Mengyun, H., & Cheng, S. (2026) · Natural Resources Research · doi:10.1007/s11053-026-10754-4
Participatory Design of AI-Based Clinical Decision Support Systems: Scoping Review
Rambach, T., Gleim, P., Heizmann, C., Kellmeyer, P., & Kunze, C. (2026) · JMIR Medical Informatics · doi:10.2196/88368
“We included studies published from 2012 onward, since this year represents a milestone in the development of deep learning, when the use of deep neural networks in image processing achieved a major breakthrough [33].”
Tiwari, S., Luitel, S., Rashidi, A., et al. (2026) · Journal of Advanced Transportation · doi:10.1155/atr/2604474
A novel design of high throughput power efficient multiply accumulate unit
Jayasanthi, M., Kalaivani, R., Sampoornam, K. P., & Nath, V. (2026) · Analog Integrated Circuits and Signal Processing · doi:10.1007/s10470-026-02629-8
Beenish, H., Javid, T., Fahad, M., Shahzad, Y., & Ishaq, F. (2023) · International Journal of Innovations in Science and Technology · doi:10.33411/ijist/565
ECA-ModNet: a parameter-efficient network for unsound wheat kernel classification
Chen, M., Ning, Y., Wen, X., et al. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2026.1902739
Gao, Q., Zhang, Y., Zhang, J., et al. (2026) · International Conference on Computer, Control and Robotics (ICCCR) · doi:10.1109/icccr69988.2026.11642711
“Advanced Driver Assistance Systems (ADAS) are shifting toward emotion-aware management, utilizing emotional states for risk assessment and intervention [7].”
Entering the agentic era of AI in drug discovery
Vijayan, R. S. K., Cross, J. B., & Poongavanam, V. (2026) · Nature Chemical Biology · doi:10.1038/s41589-026-02295-x
Detection of Covid-19 disease using CNN model
Vashisth, A., Abrol, G., David, P. K., et al. (2026) · AIP conference proceedings · doi:10.1063/5.0285795
Fast and Accurate Classification with Parallel IDK Classifier Cascades
Ratul, I. J., & Yang, K. (2026) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac69091.2026.00197
“We fine-tuned AlexNet [15], ResNet18 [9], Efficient-Net [29], ConvNeXt [23], and Swin Transformer [22] as candidate classifiers.”
Object-aware graph matching network for cross-domain remote sensing image localization
Tao, L. I. U., Ren, K., & Qian, C. H. E. N. (2026) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2026.104451
“12,13 Although effective in many settings, global feature learning often entangles stable structural cues with unstable appearance patterns, making it difficult to preserve reliable correspondences under large view-point, temporal, and modality variations.”
GPU-Accelerated Emotion-Aware Handwritten Digit Recognition With Automated Deployment on NVIDIA GPUs
Shalini, T. G., & Bharathi, S. S. K. M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0965-9.ch007
A Hybrid Classification Approach for Glaucoma Diagnosis with MultiSURF Feature Selection
Sarı, H. İ., & Keser, K. (2026) · Gazi Üniversitesi Fen Bilimleri Dergisi Part C Tasarım ve Teknoloji · doi:10.29109/gujsc.1910949
Gao, Y., Wen, Q., Ji, Y., et al. (2026) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2026.3725098
Shit, R. C. (2026) · Computer Communications · doi:10.1016/j.comcom.2026.108654
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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