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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42 Own result · 21 Own interpretation · 7 No basis given · 4 No full text of the sources · 3 Sources not found · 2 Supported · 2 Not checked yet · 1 Own method
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In the paper
“This technique reduces complex co adaptations of neurons, since a neuron cannot rely on the presence of particular other neurons.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
What the sources say
- Supportedcites [12] 12. Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012). source
The paper states on p. 1 that dropout prevents complex co-adaptations by randomly omitting hidden units, so a hidden unit cannot rely on other hidden units being present. This directly supports the citing sentence. (LLM, conf=0.95)
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In the paper
“There is, however, a very efficient version of model combination that only costs about a factor of two during training.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
What the sources say
- Supportedcites [12] 12. Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012). source
The paper describes dropout as an efficient way to combine many neural-network models (p. 1). In F.6 Training (p. 17), it reports that ImageNet training took roughly four days with dropout versus two days without it, supporting the approximate factor-of-two training cost. (LLM, conf=0.88)
plainstates a cause- Asserted: passes — The authors put it forward as true.
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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
plainstates a cause- Asserted: passes — The authors put it forward as true.
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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
plainstates a cause- Asserted: passes — The authors put it forward as true.
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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
plain- Asserted: passes — The authors put it forward as true.
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“In the field of DL, the CNN is the most famous and commonly employed algorithm [30, 71–75].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [30]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Convolutional neural networks (CNNs), built for spatial data, have demonstrated performance comparable to that of specialists in cancer detection and tumor segmentation [15,16,17,18,19,20].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [16]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Deep CNN architectures with 8, even 22 layers [4], [33], can be useful even for CADe problems where the available training datasets are limited.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [4]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Fully convolutional neural networks (fCNNs) have provided state-of-the-art performance for object classification and image segmentation (1).”
From Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning (2018) · cites this paper as [1]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The first wave saw the emergence of deep learning as the dominant paradigm for analyzing high-dimensional biological data.”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [1]
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In the paper
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
“we found that removing any convolutional layer (each of which contains no more than 1% of the model's parameters) resulted in inferior performance.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.5. Overall architecture
“The third convolutional layer has 384 kernels of size 3 × 3 × 256”
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,884
Self-citationLearning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,501
Self-citation(2010) · International Conference on Machine Learning · Cited by 13,995
Self-citationImproving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
Self-citationFukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759
LabelMe: A Database and Web-Based Tool for Image Annotation
Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0090-8 · Cited by 4,210
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196
Research works printed but not linked: 13
- Berg A., 2010, Large scale visual recognition challenge
- Deng J., 2009, CVPR09
- Krizhevsky A., 2009, Department of Computer Science
- Krizhevsky A., 2010, Convolutional deep belief networks on cifar-10. Unpublished manuscript
- Krizhevsky A., 2011, ESANN
- LeCun Y., 1990, Advances in Neural Information Processing Systems
- LeCun Y., 1985, Une procedure d'apprentissage pour reseau a seuil asymmetrique (a learning scheme for asymmetric threshold networks)
- 10.5555/1896300.1896315 · DOI
- Mensink T., 2012, Italy
- 10.5555/3104322.3104425 · DOI
- Rumelhart D.E., 1985, DTIC Document
- 10.5555/938980.939477 · DOI
- Werbos P., 1974, Beyond regression: New tools for prediction and analysis in the behavioral sciences
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Yang, Q. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.00240 · Cited by 36
Instant-Teaching: An End-to-End Semi-Supervised Object Detection FrameworkPreprint
Zhou, Q., Yu, C., Wang, Z., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.11402 · Cited by 26
A Survey on Machine Learning Techniques for Auto Labeling of Video, Audio, and Text DataPreprint
Zhang, S., Jafari, O., & Nagarkar, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.03784 · Cited by 45
Transformer-Unet: Raw Image Processing with UnetPreprint
Sha, Y., Zhang, Y., Ji, X., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.08417 · Cited by 30
Demonstration of Breast Cancer Detection Using QSVM on IBM Quantum ProcessorsPreprint
Shan, Z., Guo, J., Ding, X., Zhou, X., Wang, J., Lian, H., Gao, Y., Zhao, B., & Xu, J. (2022) · Research Square · doi:10.21203/rs.3.rs-1434074/v1 · Cited by 30
Joint Object and Part Segmentation using Deep Learned PotentialsPreprint
Wang, P., Shen, X., Lin, Z., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.00276 · Cited by 26
Computing Power and the Governance of Artificial Intelligence
Sastry, G., Heim, L., Belfield, H., Anderljung, M., Brundage, M., Hazell, J., O'Keefe, C., Hadfield, G. K., Ngo, R., Pilz, K., et al. (2024) · arXiv · doi:10.48550/arxiv.2402.08797 · Cited by 26
Deep multimodal embedding: Manipulating novel objects with point-clouds, language and trajectories
Sung, J., Lenz, I., & Saxena, A. (2017) · 2017 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2017.7989325 · Cited by 26
An End-to-End Approach to Automatic Speech Assessment for Cantonese-speaking People with Aphasia
Qin, Y., Wu, Y., Lee, T., & Kong, A. P. H. (2020) · Journal of Signal Processing Systems · doi:10.1007/s11265-019-01511-3 · Cited by 25
“The CNN model structure described in Table 2 is inspired by the AlexNet [31] and VGG [32] models.”
MethodBur, A. M., Zhang, T., Chen, X., Kavookjian, H., Kraft, S., Karadaghy, O., Farrokhian, N., Mussatto, C., Penn, J., & Wang, G. (2023) · Otolaryngology–Head and Neck Surgery · doi:10.1002/ohn.411 · Cited by 25
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications
Park, J., Samarakoon, S., Elgabli, A., Kim, J., Bennis, M., Kim, S.-L., & Debbah, M. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3055679 · Cited by 25
Reinforcement learning and artificial agency
Butlin, P. (2024) · Mind & Language · doi:10.1111/mila.12458 · Cited by 40
Object Localization Based on Proposal Fusion
Tang, S., Li, Y., Deng, L., & Zhang, Y. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2729786 · Cited by 25
Joint Deep Learning of Foreground, Background and Shape for Robust Contextual Segmentation
Ravishankar, H., Thiruvenkadam, S., Venkataramani, R., & Vaidya, V. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-59050-9_49 · Cited by 23
A Dual Attention Network with Semantic Embedding for Few-Shot Learning
Yan, S., Zhang, S., & He, X. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019079 · Cited by 22
Li, Y., Wang, F., Yang, J., Han, Z., Chen, L., Jiang, W., Zhou, H., Li, T., Tang, Z., Deng, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64894-x · Cited by 37
WAMF-FPI: A Weight-Adaptive Multi-Feature Fusion Network for UAV Localization
Wang, G., Chen, J., Dai, M. J., & Zheng, E. (2023) · Remote Sensing · doi:10.3390/rs15040910 · Cited by 29
Li, J., Chao, C., Jeong, J., Farina, J., Seri, A. R., Barry, T., Newman, H. R., Campany, M. E., Abdou, M. M., O’Shea, M., et al. (2023) · Journal of Imaging · doi:10.3390/jimaging9020048 · Cited by 28
Zhang, X., Jia, W., Sun, Y., Wang, F., & Miu, Y. (2023) · Forests · doi:10.3390/f14030483 · Cited by 26
Deep Learning-Based Segmentation of 3D Volumetric Image and Microstructural Analysis
Mahmud, B. U., Hong, G. Y., Mamun, A. A., Em, P. P., & Wu, Q. (2023) · Sensors · doi:10.3390/s23052640 · Cited by 25
Uncertainty Injection: A Deep Learning Method for Robust Optimization
Cui, W., & Yu, W. (2023) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2023.3249103 · Cited by 25
Lu, B., & Huang, B. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02105-4 · Cited by 25
Adaptive Hybrid Optimization Learning-Based Accurate Motion Planning of Multi-Joint Arm
Bai, C., Zhang, J., Guo, J., & Yue, C. P. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3262109 · Cited by 26
Cross-Level Multi-Modal Features Learning With Transformer for RGB-D Object Recognition
Zhang, Y., Yin, M., Wang, H., & Hua, C. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3275814 · Cited by 25
Accurate regularized Tucker decomposition for image restoration
Gong, W., Huang, Z., & Yang, L. (2023) · Applied Mathematical Modelling · doi:10.1016/j.apm.2023.06.031 · Cited by 25
Human, Do You Think This Painting is the Work of a Real Artist?
Park, J., Kang, H., & Kim, H. Y. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2232978 · Cited by 32
VS-Boost: Boosting Visual-Semantic Association for Generalized Zero-Shot Learning
Li, X., Zhang, Y., Bian, S., Qu, Y., Xie, Y., Shi, Z., & Jianping Fan, B. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/123 · Cited by 28
Rethinking Gradient Projection Continual Learning: Stability/Plasticity Feature Space Decoupling
Zhao, Z. W., Zhang, Z., Tan, X., Liu, J., Qu, Y., Xie, Y., & Ma, L. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00362 · Cited by 29
Metadata
- Authors
- Krizhevsky, A., Sutskever, I., & Hinton, G. E.
- Affiliations
- Google (United States) (US)
- University of Toronto (CA)
- Venue
- Communications of the ACM
- Published
- 2017-05-24
- Volume
- 60
- Issue
- 6
- Pages
- 84-90
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2163605009
- Cited by
- 112,155
- References
- 27
- Citations by year
- 2026: 2710 · 2025: 7710 · 2024: 10211 · 2023: 12267 · 2022: 12280 · 2021: 15497 · 2020: 14538 · 2019: 13976 · 2018: 9999 · 2017: 6661
- FWCI
- 5127.18
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- neural information processing systems
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- ImageNet, image classification, deep convolutional neural networks, dropout regularization, overfitting
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-11, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-11 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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