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.
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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.
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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.
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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.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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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.
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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.
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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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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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“CNN-based methods learn hierarchical representations of hand shapes and finger configurations, making them more robust to variations in lighting, background, scale, and rotation [6].”
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“This model achieved high classification success with fewer parameters compared to more complex networks such as VGG-16 and AlexNet (Krizhevsky et al. 2012).”
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Sobieraj, J., & Metelski, D. (2026) · Buildings · doi:10.3390/buildings16091720
A Lightweight and Interpretable Recurrent Vision Transformer for Robust Potato Blight Classification
Sandhya, P., & Venkataramana, B. (2026) · Potato Research · doi:10.1007/s11540-026-10058-3
“To improve the feature extraction capacity of CNNs through increased depth and residual learning, architectures such as AlexNet (Krizhevsky et al. 2017) VGGNet (Simonyan and Zisserman 2015), ResNet (He et al. 2016), and DenseNet (Huang et al. 2017) have been developed (Ferentinos 2018).”
Automated classification of export-grade bananas using CNNs and the cheetah optimization algorithm
Yasin, E. T., Eser, M., Bi̇lgi̇n, M., et al. (2026) · Journal of Food Measurement & Characterization · doi:10.1007/s11694-026-04396-6
“It is the hierarchical structure of CNNs that enables them to learn intricate patterns from raw input data, making them highly effective for image classification tasks [39 , 40].”
Naznine, M., Chowdhury, M. E., Ali, S. H. M., Ibrahim, M. F., Reaz, M. B. I., Meeran, A. B. M., & Sankaran, R. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114883
Sparse4D: Sparse-Based End-to-End Multi-Sensor Temporal Perception
Lin, X., Pei, Z., Li, K., Lin, T., Huang, L., Yu, C., See, J., Su, Z., & Yang, C. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3688545
A Deep Multialgorithm Facial Biometric System
Mehraj, H., & Mir, A. H. (2025) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661825700579
Ning, S., Ding, F., Chen, B., & Guo, P. (2026) · Scientific Reports · doi:10.1038/s41598-026-50869-5
“In 2012, the AlexNet architecture proposed by Krizhevsky et al. 23 marked the formal emergence of deep learning based on Convolutional Neural Networks (CNNs) as the mainstream approach for visual tasks.”
MCM-DeepFuzz: fuzzing for DNNs with multiple criteria and mutation
He, J., Zhao, P., Ma, R., & Liu, J. (2026) · Pattern Analysis and Applications · doi:10.1007/s10044-026-01678-z
FAST-GOAL: Fast and Efficient Global-Local Object Alignment Learning
Choi, H., Jang, Y. K., & Eom, C. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3687083
“Table XII demonstrates performance on MSCOCO [9] and Flickr30k [10] datasets using standard R@1/5/10/15 metrics.”
McCuskee, S., Liu, Z., Lee, H.-C., Duran, L. M., Bellis, J., Haq, A., Curtis, S. A., Liu, A., Chui, T. Y. P., ROSEN, R., et al. (2026) · Blood Advances · doi:10.1182/bloodadvances.2025017513
“35 To evaluate multimodal performance, clinical and OCT-A models were trained and evaluated using fivefold crossvalidation with a split at the patient level (to avoid data leakage).”
An improved ResNet50-based method for strawberry classification by physical quality
Huang, S.-F., Niu, L., Liu, C., et al. (2026) · Journal of Food Measurement & Characterization · doi:10.1007/s11694-026-04392-w
“Several widely used deep learning models—ResNet34, ResNet50, VGG16 [ 41 ], DenseNet121 [42], and AlexNet [43] —were selected as baseline controls.”
A Lightweight Road Target Detection Algorithm based on YOLO11
Wang, P., Chen, J., & Pang, Z. (2025) · China Automation Congress (CAC) · doi:10.1109/cac67268.2025.11486983
“Among the pioneering advances in this field, the advent of AlexNet [5] constituted a pivotal breakthrough, which spurred the extensive application of convolutional neural networks (CNNs) in object detection tasks.”
Sikder, M., Kayser, S. M. M., Miah, M. D. H., & Alam, S. (2026) · International Conference on Emerging Smart Computing and Informatics (ESCI) · doi:10.1109/esci68015.2026.11493229
AlexNet-LSTM-SVM Hybrid Architecture for Lung Disease Classification
Khaing, E. E. (2026) · International Conference on Emerging Smart Computing and Informatics (ESCI) · doi:10.1109/esci68015.2026.11493407
“Despite their strong performance, CNNs alone may not fully capture sequential relationships such as inter-slice dependencies in CT scans or structured spatial patterns within an image leading to limitations in classification accuracy [2].”
LAPAmix: Label-Adaptive Phase-Amplitude Mixup for Time-Series Augmentation
Zhang, H., Ji, X., & Fu, J. (2025) · China Automation Congress (CAC) · doi:10.1109/cac67268.2025.11487757
NanoDistillNet: a lightweight diagnostic model for PV module faults in UAV-based inspection
Ma, W., Zhang, Z., & Zhang, X. (2026) · Frontiers in Energy Research · doi:10.3389/fenrg.2026.1742642
Conservative Metric Learning for Generalized Zero-Shot Learning
Li, X., Zhang, X., Qu, Y., et al. (2025) · China Automation Congress (CAC) · doi:10.1109/cac67268.2025.11487132
CherryNet: A Lightweight Classification Model for External Quality Grading of Cherry
Guo, Y., He, J., & Bi, Y. (2025) · China Automation Congress (CAC) · doi:10.1109/cac67268.2025.11487412
“The CherryNet model we proposed in this experiment is a lightweight convolutional neural network model [18], which is especially suitable for resource-constrained scenarios such as mobile devices and embedded systems[19], and is of great significance for lightweight model [20]”
Toward Automated Deep Learning: Advances and Challenges in Neural Architecture Search
Ren, J., Wan, L., & Xiong, L. (2026) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70091 · Cited by 1
“…network architectures, historically designed through human expertise—exemplified by the progression from LeNet5 (Lecun and Bottou 1998) and AlexNet (Krizhevsky et al. 2012) to VGGNet (Simonyan and Zisserman 2015), GoogLeNet (Szegedy et al. 2014), MobileNets (Howard et al. 2017), ResNet (He et al.…”
InfluentialDual attention enhancement network for Chinese herbal medicine classification
Fu, M., & Hong, H. (2026) · Electronic Research Archive · doi:10.3934/era.2026165
Slice-Level Detection of Cervical Vertebral Fractures from CT Images Using a Custom CNN
Mohan, J., Sadasivuni, S. T., Chinimilli, V. R. P., Nukala, R., Vure, S., & Chinnala, S. (2026) · IEEE International Conference on Intelligent Systems, Smart and Green Technologies (ICISSGT) · doi:10.1109/icissgt67741.2026.11483997
“To address the imbalance between fracture-positive and fracture-negative slices, and to maintain computational efficiency, we design a lightweight 2D CNN architecture inspired by conventional medical image CNN backbones [11], [15].”
ODC-KAN: Ocular Disease Classification Using Kolmogorov-Arnold Networks From Fundus Images
Preity, Bhandari, A., & Shahnawazuddin, S. (2026) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2026.3683731
Multifrequency Neural Contrast Source Inversion for Solving Inverse Scattering Problems: 2-D Cases
Shan, T., Zeng, J., Yang, L., et al. (2026) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2026.3686504
AstroSight: Galaxy Morphology Classification with Multimodal Large Language Models
He, B., Zhao, F., Wang, Y., Chen, P., Wang, X., Ma, D., Xu, H., & Li, H. (2026) · Publications of the Astronomical Society of the Pacific · doi:10.1088/1538-3873/ae5f46
Breast cancer prediction from breast ultrasound images using deep learning
Rashid, S. M. H. O., Mittal, H., Thompson, M., et al. (2026) · 2026 International Conference on Electrical/Electronics, Robotics, Artificial Intelligence, and Informatics (ICERAI) · doi:10.1109/icerai69511.2026.11494592
Liu, Y., Chernikov, F., Grant‐Jacob, J. A., et al. (2026) · Machine Learning Engineering · doi:10.1088/3049-4761/ae6708
“Two images are first passed through a CNN. AlexNet [36] was selected as it has been shown to provide strong performance.”
Intensively Quantized Integrated Fuzzy Neural Network for High-Accuracy Image Classification
Yao, J., & Guo, Y. (2026) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2026.3689929
Feng, W. A. N. G., Li, X., Shan, X., Huang, H., Jiao, C., & JIANG, J. (2026) · Journal of Advanced Mechanical Design Systems and Manufacturing · doi:10.1299/jamdsm.2026jamdsm0013
CaPTQ: Calibration Data Selection for Visual Services Based on Post-Training Quantization
Zhang, J., Yang, P., Ding, C., Raza, S., Zhao, Q., Yuan, P., & Wang, S. (2026) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2026.3689186
EdgeSecure-AI: A Multimodal Edge-Intelligent Threat Detection System for Smart CPS Surveillance
Raghul, S., Rohit, R., & Priya, S. (2026) · doi:10.1109/ccic68129.2026.11485972
Towards Efficient Cross-Scene Activity Recognition: Lightweight CNNs with Wi-Fi CSI
Huan, L., & Zhao, J. (2026) · Proceedings of the 2026 2nd International Conference on Big Data, Communication Technology and Computer Applications · doi:10.1145/3801359.3801412
“VGG [36] and AlexNet [37], which are CNN networks widely used, and their recognition accuracy can reach 91.86 % and 95.56 % respectively.”
InfluentialTransparent Vision: A Theory of Hierarchical Invariant Representations
Qi, S., Zhang, Y., Wang, C., Xia, Z., Cao, X., & Fan, F. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00328
“…time-frequency resolutions; • Advanced over-complete representations: 1) typical CNNs, i.e. , direct-learning CNNs (denoted as Sim-pleNet), transfer-learning AlexNet [19] and VGGNet [37], with also data augmentation (denoted as ‘+’); 2) invariant CNNs, i.e. , scattering networks [6] and our HIR.”
Drawing Developmental Trajectory From Cortical Surface Reconstruction
Wu, W., Qu, R., Liu, Z., et al. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01026
“With the rapid advancement of deep learning [8, 13, 17, 19, 29], numerous studies [1, 2, 5, 14, 20, 33] have integrated deep neural networks, enabling precise reconstruction of cortical surfaces within a few seconds.”
An approach for optimizing Convolutional Neural Network in copper foil surface defect detection
Zhang, H., Piao, Z., Wang, J. J., et al. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114941
Hamza, S. A., Ali, M., Ahmed, I., Ijaz, U., Kim, T., & Kim, H. (2026) · Materials Today Nano · doi:10.1016/j.mtnano.2026.100835
Image classification of archaeological artifacts using quantum neural networks
Guingo, B. C., de Souza, L. C., da Silva, E. K., Cisneiros, D., & Portugal, R. (2026) · Digital Applications in Archaeology and Cultural Heritage · doi:10.1016/j.daach.2026.e00539
Jariwala, K. V., Kubavat, A. K., Patel, K. V., Rathod, K. V., & Bhimda, V. R. (2026) · doi:10.5005/jihr-11055-0017
Neural Networks for Bayesian Inverse Problems Governed by a Nonlinear ODE
Villalobos, G., Rudi, J., & Mang, A. (2026) · SIAM Journal on Scientific Computing · doi:10.1137/24m1711480
PanoKernel: Large Distortion-Aware Kernel for Panoramic Depth Perception
Yan, Z., Shen, Z., Yuan, J., Li, X., Li, J., & Yang, J. J. (2026) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2026.3683506
NOGUCHI, H. (2025) · Journal of the Society of Biomechanisms · doi:10.3951/sobim.49.2_140
Leveraging 3D Heart Visualisation and Data Balancing Techniques for ECG Classification
Amara, K., Kerdjidj, O., Guerroudji, M. A., Atalla, S., & Ramzan, N. (2026) · Bioengineering · doi:10.3390/bioengineering13050525
Quality of information: effective estimation of web service usefulness by selecting test inputs
Li, Z., Song, Z., Yu, W., et al. (2026) · Service Oriented Computing and Applications · doi:10.1007/s11761-026-00492-z
Wang, Z., Li, W., & Sun, K. (2026) · Remote Sensing · doi:10.3390/rs18091422
“Transformer-style attention mechanisms have introduced crucial global contextual reasoning, although CNN-based architectures excel at local feature extraction [6–11].”
Efficient convolution-based model pruning with learn gates & BN and channel interdependence learning
Chen, B., Chen, M.-X., & Tsai, C. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133772
Performance Evaluation of a 1D CNN for Waveform Classification in Additive White Gaussian Noise
Getu, B. N., & Semu, N. K. (2026) · International Conference on Electrical/Electronics, Robotics, Artificial Intelligence, and Informatics (ICERAI) · doi:10.1109/icerai69511.2026.11494558
“Among the various neural architectures, Convolutional Neural Networks (CNNs) have emerged as a dominant framework for classification tasks because they efficiently exploit local correlations through convolutional filtering, parameter sharing, and hierarchical feature abstraction [9], [10].”
Lal, B., Alagarsamy, R., Fatima, K., Ankush, A., & Das, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45004-4.00004-9
AI-driven pathology: Transforming histopathological analysis
Elmeligy, A. O., Haider, Y., Elemam, N. M., & Talaat, I. M. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45004-4.00017-7
Large Learning Rates Simultaneously Achieve Robustness to Spurious Correlations and Compressibility
Barsbey, M., Prieto, L., Zafeiriou, S. P., & Birdal, T. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00199
“Batch sizes are set to 16 for CelebA, Water-birds, and ImageNet experiments, and 100 for the remaining experiments.”
InfluentialLiang, Y., Yang, Y., Shan, X., & Zhang, M. (2026) · Journal of Aircraft · doi:10.2514/1.c038652
Kaluthantrige, A., Feng, J., Domenico, G. D., & Gil-Fernández, J. (2026) · Advances in Space Research · doi:10.1016/j.asr.2026.04.105
He, K., Zhang, J., & Zhou, H. (2026) · Thin-Walled Structures · doi:10.1016/j.tws.2026.115056
Atherton, D. S., Cueto, S. M. A., & Bodduluri, S. (2026) · American Journal of Forensic Medicine & Pathology · doi:10.1097/paf.0000000000001135
Wang, G., Sun, Y., Zhu, P., et al. (2026) · Journal of Environmental Management · doi:10.1016/j.jenvman.2026.129826
Yu, H., Yang, X., Li, R., Tang, J., Hubacek, K., & Shan, Y. (2026) · Environmental Research Climate · doi:10.1088/2752-5295/ae6887 · Cited by 1
“Convolutional Neural Networks (CNN), renowned for their local connectivity and weight-sharing mechanisms, offer reduced model complexity and the capacity to automatically extract hierarchical spatial features [32].”
Banerjee, S. K., Das, K., Das, M., & Deb, M. (2025) · International Conference on Emerging Trends in Electronic Devices and Computational Techniques (EDCT) · doi:10.1109/edct65302.2025.11495713
“Pre-trained weights from the ImageNet dataset [7] were used to initialize the model.”
Object Tracking and Shooting Turret
T., K. A., Murukan, V., P, A., et al. (2026) · 2026 IEEE International Conference on Emerging Computing and Intelligent Technologies (ICoECIT) · doi:10.1109/icoecit68303.2026.11496998
“Convolutional Neural Networks (CNNs) and their derivatives execute the analysis of visual data very fast and accurate thus they can easily face any environmental conditions variation during the detection [6], [7].”
Crack Pattern Identification in Masonry Buildings via Remote Sensing and Deep Learning Technique
Candela, F., Percolla, G., Lasorella, M., et al. (2026) · Advances in Science, Technology & Innovation/Advances in science, technology & innovation · doi:10.1007/978-3-032-09145-1_27
Zero-Shot Multimodal Part Segmentation for Space Target via 3D-2D Semantic Priors Transfer
Feng, C., Gu, X., Wang, B., & Yang, X. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3689873
An Approach for the Classification of Images of Different Spatial Sizes
Singla, N., & Tillo, T. (2026) · IEEE Conference on Computer Applications (ICCA) · doi:10.1109/icca69280.2026.11485813
Feedback Alignment Meets Low-Rank Manifolds: A Structured Recipe for Local Learning
Roy, A., Apolinario, M., Biswas, S. D., et al. (2025) · IEEE Workshop/Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00292 · Cited by 1
“An ablation study is conducted to assess the contribution of individual loss components.”
Vision Compass for Drones: An Alternative Use of a Limitation in Vision Models for Localization
Warnakulasooriya, K., & Segev, A. (2026) · International Conference on Image Processing and Robotics (ICIPRoB) · doi:10.1109/iciprob69625.2026.11497794
“The research employed 8 vision models such as AlexNet [19], ResNet-18, ResNet-50 [20], Shufflenet [21], VGG [22], ConvNext [23], MobileNetV2 [24], ResNeXt [25].”
Li, L., Li, M., Zhang, X., et al. (2026) · Applied Optics · doi:10.1364/ao.596456
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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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