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)
plainstates a cause- Asserted: passes — The authors put it forward as true.
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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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Peng, C., Li, B., Zou, K., Zhang, B., Dai, G., & Tsoi, A. C. (2024) · Sensors · doi:10.3390/s24227404 · Cited by 12
AI-assisted diagnosis of vulvovaginal candidiasis using cascaded neural networks
Wang, Z., Wang, R., Guo, H., Zhao, Q., Ren, H., Niu, J., Wang, Y., Wu, W., Liang, B., Yi, X., et al. (2024) · Microbiology Spectrum · doi:10.1128/spectrum.01691-24 · Cited by 15
Zou, C., Liu, Y., Yang, Y., Zhou, C., Yang, Y. F., & Shang, Y. (2024) · Signal Processing · doi:10.1016/j.sigpro.2024.109811 · Cited by 12
Planetary Gearboxes Fault Diagnosis Based on Markov Transition Fields and SE-ResNet
Liu, Y., Gao, T., Wu, W., et al. (2024) · Sensors · doi:10.3390/s24237540 · Cited by 11
Severe-hail detection with C-band dual-polarisation radars using convolutional neural networks
Forcadell, V., Augros, C., Caumont, O., Dedieu, K., Ouradou, M., David, C., Ventura, J. F. I., Laurantin, O., & Al-Sakka, H. (2024) · Atmospheric measurement techniques · doi:10.5194/amt-17-6707-2024 · Cited by 17
Automated Shoreline Segmentation in Satellite Imagery Using USV Measurements
Jaszcz, A., Włodarczyk-Sielicka, M., Stateczny, A., et al. (2024) · Remote Sensing · doi:10.3390/rs16234457 · Cited by 11
Vision Sensing-Driven Intelligent Ocular Disease Detection Using Conformer-Based Dual Fusion
Guo, Z., Zhang, Q., Xu, P., Shen, Y., Chakraborty, C., Alfarraj, O., & Yu, K. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3510298 · Cited by 11
Steel surface defect detection based on sparse global attention transformer
Li, Y., Han, Z., Wang, W., Xu, H., Wei, Y., & Zai, G. (2024) · Pattern Analysis and Applications · doi:10.1007/s10044-024-01375-9 · Cited by 11
Research on temperature prediction of shearer cable based on bidirectional long short-term memory
Zhao, L., Lin, G., Wang, Y., Xie, B., Wan, C., Zhang, H., Tian, S., Bai, Z., Zhang, M., & Jin, X. (2024) · International Journal of Thermal Sciences · doi:10.1016/j.ijthermalsci.2024.109597 · Cited by 13
3D-CNN with Multi-Scale Fusion for Tree Crown Segmentation and Species Classification
Wang, J., Zhen, Z., Zhao, Y., Ma, Y., & Zhao, Y. (2024) · Remote Sensing · doi:10.3390/rs16234544 · Cited by 12
Fan, H., Chen, B., Cao, X., Li, Q., Xu, H., Zhang, T., Zhang, X., & Ren, Y. (2024) · Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · doi:10.1177/09544062241293355 · Cited by 11
Towards food security with the Grain Shield web application for stored grain pest identification
Santhanambika, M. S., & Maheswari, G. (2024) · Journal of Stored Products Research · doi:10.1016/j.jspr.2024.102515 · Cited by 11
Multi-task convolutional deep neural network for recommendation based on knowledge graphs
Jiang, M., Li, M., Cao, W., Yang, M., & Zhou, L. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129136 · Cited by 12
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Stausberg, L., Schmitz, B., Klingbeil, L., et al. (2024) · Remote Sensing · doi:10.3390/rs16244720 · Cited by 11
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Mastropaolo, A., Escobar‐Velásquez, C., & Linares‐Vásquez, M. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3709360 · Cited by 24
Hadroug, N., Amari, A. S., Alayed, W. M., et al. (2024) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2024.100760 · Cited by 11
Li, W., Wang, X., Li, G., Geng, B., & Varshney, P. K. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3524639 · Cited by 16
Semi-Patchcore: A Novel Two-Staged Method for Semi-Supervised Anomaly Detection and Localization
Xie, S., Wu, X., & Wang, M. Y. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3527588 · Cited by 13
Guo, X., Wang, J., Gao, G., et al. (2025) · Frontiers in Plant Science · doi:10.3389/fpls.2024.1509656 · Cited by 11
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Canelas, J., Clementino, L., Cid, A., et al. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00704-9 · Cited by 11
Ullah, M., Qiu, H., Huangfu, W., Yang, D., Wei, Y., & Tang, B. (2025) · Land · doi:10.3390/land14010172 · Cited by 14
Jia, Z. (2025) · IEEE Access · doi:10.1109/access.2025.3530242 · Cited by 13
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Li, M., Li, Y., & Li, Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2024.129322 · Cited by 11
Zhang, X., Razavi‐Far, R., Isah, H., David, A., Higgins, G., & Zhang, M. Q. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.112965 · Cited by 15
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Corso, G. D., Colantonio, S., & Caudai, C. (2025) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2025.107548 · Cited by 13
CenterNet-Elite: A Small Object Detection Model for Driving Scenario
Wang, L., Li, X., Chen, X., & Zhou, B. (2025) · IEEE Access · doi:10.1109/access.2025.3532786 · Cited by 12
Garcia, A. D. B., Islam, M. S., Prudente, V. H. R., et al. (2025) · Applied Computing and Geosciences · doi:10.1016/j.acags.2025.100223 · Cited by 11
Metadata
- Authors
- Krizhevsky, A., Sutskever, I., & Hinton, G. E.
- Affiliations
- Google (United States) (US)
- University of Toronto (CA)
- Venue
- Communications of the ACM
- Published
- 2017-05-24
- Volume
- 60
- Issue
- 6
- Pages
- 84-90
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2163605009
- Cited by
- 112,155
- References
- 27
- Citations by year
- 2026: 2710 · 2025: 7710 · 2024: 10211 · 2023: 12267 · 2022: 12280 · 2021: 15497 · 2020: 14538 · 2019: 13976 · 2018: 9999 · 2017: 6661
- FWCI
- 5127.18
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- neural information processing systems
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- ImageNet, image classification, deep convolutional neural networks, dropout regularization, overfitting
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-11, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-11 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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