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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
42 Own result · 21 Own interpretation · 7 No basis given · 4 No full text of the sources · 3 Sources not found · 2 Supported · 2 Not checked yet · 1 Own method
Every source the paper cites for this claim was checked and supports it.
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.
- 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.
- Supported
Every source the paper cites for this claim was checked and supports it.
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.
- 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.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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.
- 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.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
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.
- 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.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
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.
- 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. 18
- Probably not a substantial claim. 26
- The quote only partly states this claim. 2
Create a free account to see these claims.
Create a free account to see these assumptions.
34 more claims
Create a free account to see all 82 claims and to verify the 3 references still open.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In the field of DL, the CNN is the most famous and commonly employed algorithm [30, 71–75].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [30]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Convolutional neural networks (CNNs), built for spatial data, have demonstrated performance comparable to that of specialists in cancer detection and tumor segmentation [15,16,17,18,19,20].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [16]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Deep CNN architectures with 8, even 22 layers [4], [33], can be useful even for CADe problems where the available training datasets are limited.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [4]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Fully convolutional neural networks (fCNNs) have provided state-of-the-art performance for object classification and image segmentation (1).”
From Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning (2018) · cites this paper as [1]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The first wave saw the emergence of deep learning as the dominant paradigm for analyzing high-dimensional biological data.”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [1]
Experimental — accuracy not yet measured
19 more claims about this paper
Create a free account to see every claim other papers make about this one.
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
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
Cited by
Items 31,701–31,800 of 108,435
ECG Data Analysis with Denoising Approach and Customized CNNs
Mishra, A., Dharahas, G., Gite, S., et al. (2022) · Sensors · doi:10.3390/s22051928 · Cited by 26
Ueda, D., Yamamoto, A., Onoda, N., Takashima, T., Noda, S., Kashiwagi, S., Morisaki, T., Fukumoto, S., Shiba, M., Morimura, M., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0265751 · Cited by 29
Segmentation and Quantitative Analysis of Photoacoustic Imaging: A Review
Le, T. D., Kwon, S. Y., & Lee, C. (2022) · Photonics · doi:10.3390/photonics9030176 · Cited by 27
Konnaris, M. A., Brendel, M., Fontana, M. A., Otero, M., Ivashkiv, L. B., Wang, F., & Bell, R. D. (2022) · Arthritis Research & Therapy · doi:10.1186/s13075-021-02716-3 · Cited by 26
Alnowami, M. R., Abolaban, F. A., & Taha, E. M. (2022) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2022.01.003 · Cited by 29
Prakash, A. J., & Prakasam, P. (2022) · The Visual Computer · doi:10.1007/s00371-022-02443-z · Cited by 26
Karim, M., Missen, M. M. S., Umer, M., Sadiq, S., Mohamed, A., & Ashraf, I. (2022) · Applied Sciences · doi:10.3390/app12063203 · Cited by 26
Multiview Wasserstein generative adversarial network for imbalanced pearl classification
Gao, S. L., Dai, Y., Li, Y., Liu, K., Chen, K., & Liu, Y. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac6224 · Cited by 26
Designing efficient convolutional neural network structure: A survey
Mi, J., Feng, J., & Huang, K.-Y. (2022) · Neurocomputing · doi:10.1016/j.neucom.2021.08.158 · Cited by 28
Galdames, F. J., Perez, C. A., Estévez, P. A., et al. (2022) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2022.104538 · Cited by 26
Resource Consumption for Supporting Federated Learning in Wireless Networks
Liu, Y., Qin, S., Sun, Y., & Feng, G. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3181611 · Cited by 26
Abubakr, M., Akoush, B. M., Khalil, A., & Hassan, M. A. (2022) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-022-02666-y · Cited by 26
Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation
Wei, X., Yu, H., Hu, Y., Weng, R., Luo, W., & Jin, R. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.546 · Cited by 31
Deep neural networks and image classification in biological vision
Leek, E. C., Leonardis, A., & Heinke, D. G. (2022) · Vision Research · doi:10.1016/j.visres.2022.108058 · Cited by 29
Deep learning-based point-spread function deconvolution for migration image deblurring
Liu, C., Sun, M., Dai, N., Wu, W., Wei, Y., Guo, M., & Fu, H. (2022) · Geophysics · doi:10.1190/geo2020-0904.1 · Cited by 27
Labenski, P., Ewald, M., Schmidtlein, S., & Fassnacht, F. E. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.102799 · Cited by 26
Owais, M., Baek, N. R., & Park, K. R. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117360 · Cited by 27
Kong, N. C. L., Margalit, E., Gardner, J. L., & Norcia, A. M. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009739 · Cited by 21
An intelligent facial expression recognition system with emotion intensity classification
Saxena, S., Tripathi, S., & Sudarshan, T. S. B. (2022) · Cognitive Systems Research · doi:10.1016/j.cogsys.2022.04.001 · Cited by 29
Hu, S., Pan, Z., Dong, J., et al. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2022.3158199 · Cited by 26
Obscenity Detection in Videos Through a Sequential ConvNet Pipeline Classifier
Gautam, N., & Vishwakarma, D. K. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2022.3158613 · Cited by 26
Evolving Optimised Convolutional Neural Networks for Lung Cancer Classification
Pfeffer, M. A., & Ling, S. H. (2022) · Signals · doi:10.3390/signals3020018 · Cited by 28
Generalizing Adversarial Explanations with Grad-CAM
Chakraborty, T., Trehan, U., Mallat, K., & Dugelay, J. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00031 · Cited by 30
Automatic Tuberculosis and COVID-19 cough classification using deep learning
Pahar, M., Klopper, M., Reeve, B. W. P., Warren, R. M., Theron, G., Diacon, A. H., & Niesler, T. (2022) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet55527.2022.9873469 · Cited by 28
An improved categorical cross entropy for remote sensing image classification based on noisy labels
Li, P., He, X., Cheng, X., et al. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117296 · Cited by 26
Deep Learning Analysis of the Adipose Tissue and the Prediction of Prognosis in Colorectal Cancer
Lin, A., Qi, C., Li, M., Guan, R., Имянитов, Е. Н., Mitiushkina, N. V., Cheng, Q., Liu, Z., Wang, X., Lyu, Q., et al. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2022.869263 · Cited by 27
Chen, S., Du, X., Zhao, W., Guo, P., Chen, H., Jiang, Y., & Wu, H. (2022) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2022.121418 · Cited by 25
Wu, H., Souedet, N., Jan, C., Clouchoux, C., & Delzescaux, T. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106180 · Cited by 28
Pan, S., & Ahamed, T. (2022) · Sensors · doi:10.3390/s22114187 · Cited by 30
A New Deep Learning Neural Network Model for the Identification of InSAR Anomalous Deformation Areas
Zhang, T., Zhang, W., Cao, D., et al. (2022) · Remote Sensing · doi:10.3390/rs14112690 · Cited by 26
Li, S., Chen, W., Bhandari, K. S., Jung, D. W., & Chen, X. (2022) · Materials · doi:10.3390/ma15113788 · Cited by 26
Sun, K., He, M., He, Z., Liu, H., & Pi, X. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103768 · Cited by 27
Islam, M. S., Tabassum, M., Nishat, M. M., Faisal, F., & Hasan, M. S. (2022) · International Conference on Business and Industrial Research (ICBIR) · doi:10.1109/icbir54589.2022.9786472 · Cited by 26
Deep Transfer Learning for the Multilabel Classification of Chest X-ray Images
Huang, G., Fu, Q.-J., Gu, M.-Z., Lu, N., Liu, K., & Chen, T. (2022) · Diagnostics · doi:10.3390/diagnostics12061457 · Cited by 30
A perspective on the diagnosis of cracked tooth: imaging modalities evolve to AI-based analysis
Guo, J., Wu, Y., Chen, L., Long, S., Chen, D., Ouyang, H., Zhang, C., Tang, Y., & Wang, W. (2022) · BioMedical Engineering OnLine · doi:10.1186/s12938-022-01008-4 · Cited by 27
An improved self-supervised learning for EEG classification
Ou, Y., Sun, S., Gan, H., Zhou, R., & Yang, Z. Q. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022325 · Cited by 26
Erişti, B., & Erişti, H. (2022) · International Transactions on Electrical Energy Systems · doi:10.1155/2022/8519379 · Cited by 33
Barely-Supervised Learning: Semi-supervised Learning with Very Few Labeled Images
Lucas, T., Weinzaepfel, P., & Rogez, G. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20082 · Cited by 27
Zhu, Q., Sun, Y., Guan, Q., Wang, L., & Lin, W. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3170335 · Cited by 26
Yan, G., Schmitz, A., Funabashi, S., Somlor, S., Tomo, T. P., & Sugano, S. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3151260 · Cited by 26
Computational Emotion Analysis From Images: Recent Advances and Future Directions
Zhao, S., Huang, Q., Tang, Y., Yao, X., Yang, J., Ding, G., & Schuller, B. W. (2021) · Springer eBooks · doi:10.1007/978-3-030-81465-6_4 · Cited by 27
Multilevel Hierarchical Network with Multiscale Sampling for Video Question Answering
Peng, M., Wang, C., Gao, Y., Shi, Y., & Zhou, X.-D. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/178 · Cited by 31
An adaptive polyak heavy-ball method
Saab, S., Phoha, S., Zhu, M., et al. (2022) · Machine Learning · doi:10.1007/s10994-022-06215-7 · Cited by 26
Image Captioning - A Deep Learning Approach
Jain, U. (2022) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2022.45638 · Cited by 26
Literature review of vision‐based dynamic gesture recognition using deep learning techniques
Jain, R., Karsh, R. K., & Barbhuiya, A. A. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7159 · Cited by 25
Deep Contrastive Self-Supervised Hashing for Remote Sensing Image Retrieval
Tan, X., Zou, Y., Guo, Z., Zhou, K., & Yuan, Q. (2022) · Remote Sensing · doi:10.3390/rs14153643 · Cited by 28
Sar2color: Learning Imaging Characteristics of SAR Images for SAR-to-Optical Transformation
Guo, Z., Guo, H., Liu, X., Zhou, W., Wang, Y., & Fan, Y. (2022) · Remote Sensing · doi:10.3390/rs14153740 · Cited by 28
End-to-end concrete appearance analysis based on pixel-wise semantic segmentation and CIE Lab
Hao, Z., & Qi, X. (2022) · Cement and Concrete Research · doi:10.1016/j.cemconres.2022.106926 · Cited by 26
Xu, C., Peng, Y., Zhu, W., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.969907 · Cited by 25
Chen, J., Han, J., Liu, C., et al. (2022) · Symmetry · doi:10.3390/sym14081671 · Cited by 26
Wang, S., Zheng, H., Tang, L., Li, Z., Zhao, R., Lu, Y., & Aw, K. C. (2022) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-022-00616-x · Cited by 26
Huang, J., Huang, Z., Fang, X., Feng, S., Chen, X., Liu, J., Yuan, H., & Wang, H. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557091 · Cited by 29
Feng, C., Wang, S., & Li, Z. (2022) · Journal of Environmental Management · doi:10.1016/j.jenvman.2022.115966 · Cited by 26
EdgeFaaSBench: Benchmarking Edge Devices Using Serverless Computing
Rajput, K. R., Kulkarni, C. D., Cho, B., Wang, W., & Kim, I. K. (2022) · IEEE International Conference on Edge Computing and Communications (EDGE) · doi:10.1109/edge55608.2022.00024 · Cited by 29
Enabling efficient deep convolutional neural network-based sensor fusion for autonomous driving
Zeng, X., Wang, Z., & Hu, Y. (2022) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/3489517.3530444 · Cited by 31
Hybrid of Free Space Optics Communication and Sensor System Using IWDM Technique
Hayle, S. T., Manie, Y. C., Yao, C.-K., et al. (2022) · Journal of Lightwave Technology · doi:10.1109/jlt.2022.3186895 · Cited by 26
Röglin, J., Ziegeler, K., Kube, J., et al. (2022) · Frontiers in Computer Science · doi:10.3389/fcomp.2022.858874 · Cited by 26
Kamari, M., Kim, J., & Ham, Y. (2022) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0001044 · Cited by 26
Bai, N., Nourian, P., Luo, R., & Roders, A. P. (2022) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11090469 · Cited by 32
A Wafer Surface Defect Detection Method Built on Generic Object Detection Network
Wang, X., Jia, X., Jiang, C., & Jiang, S. (2022) · Digital Signal Processing · doi:10.1016/j.dsp.2022.103718 · Cited by 29
Godoy, R. V., Dwivedi, A., Guan, B., Turner, A., Shieff, D., & Liarokapis, M. V. (2022) · IEEE Access · doi:10.1109/access.2022.3206436 · Cited by 28
Discriminating Earthquakes From Quarry Blasts Using Capsule Neural Network
Saad, O. M., Soliman, M. S., Chen, Y., et al. (2022) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2022.3207238 · Cited by 26
A voting-based ensemble feature network for semiconductor wafer defect classification
Misra, S., Kim, D., Kim, J., Shin, W., & Kim, C. (2022) · Scientific Reports · doi:10.1038/s41598-022-20630-9 · Cited by 26
Apple-YOLO: A Novel Mobile Terminal Detector Based on YOLOv5 for Early Apple Leaf Diseases
Li, J., Zhu, X., Jia, R., Liu, B., & Yu, C. (2022) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac54236.2022.00056 · Cited by 29
Li, J., Li, K., & Tang, S. (2022) · Soil Dynamics and Earthquake Engineering · doi:10.1016/j.soildyn.2022.107560 · Cited by 26
DEAU-Net: Attention networks based on dual encoder for Medical Image Segmentation
Fu, Z., Li, J., & Hua, Z. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106197 · Cited by 26
Dagon, K., Truesdale, J., Biard, J. C., Kunkel, K. E., Meehl, G. A., & Molina, M. J. (2022) · Journal of Geophysical Research Atmospheres · doi:10.1029/2022jd037038 · Cited by 27
Na, J., Zhang, H., Lian, J., & Zhang, B. (2022) · Applied Sciences · doi:10.3390/app122010619 · Cited by 27
Understanding and defending against White-box membership inference attack in deep learning
Wu, D., Qi, S., Qi, Y., Li, Q., Cai, B., Guo, Q., & Cheng, J. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110014 · Cited by 31
Surface Characteristics Measurement Using Computer Vision: A Review
Hashmi, A. W., Mali, H. S., Meena, A., Hashmi, M. F., & Bokde, N. D. (2022) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.021223 · Cited by 26
Kumar, A., & Gorai, A. K. (2022) · Computers & Geosciences · doi:10.1016/j.cageo.2022.105251 · Cited by 28
Temporal-based Swin Transformer network for workflow recognition of surgical video
Pan, X., Gao, X., Wang, H., Zhang, W., Mu, Y., Bi, M., & He, X. (2022) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-022-02785-y · Cited by 28
AQE-Net: A Deep Learning Model for Estimating Air Quality of Karachi City from Mobile Images
Ahmed, M., Shen, Y., Ahmed, M., Xiao, Z., Cheng, P., Ali, N., Ghaffar, A., & Ali, S. (2022) · Remote Sensing · doi:10.3390/rs14225732 · Cited by 29
Physics Informed Neural Network using Finite Difference Method
Lim, K. L., Dutta, R., & Rotaru, M. D. (2022) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53654.2022.9945171 · Cited by 26
Cataract Detection and Grading Using Ensemble Neural Networks and Transfer Learning
Maaliw, R. R., III, Alon, A. S., Lagman, A. C., et al. (2022) · IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) · doi:10.1109/iemcon56893.2022.9946550 · Cited by 26
Liang, Y., Zhu, L., Wang, X., & Yang, Y. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3213563 · Cited by 26
Development and performance evaluation of a deep learning lung nodule detection system
Katase, S., Ichinose, A., Hayashi, M., Watanabe, M., Chin, K., Takeshita, Y., Shiga, H., Tateishi, H., Onozawa, S., Shirakawa, Y., et al. (2022) · BMC Medical Imaging · doi:10.1186/s12880-022-00938-8 · Cited by 30
Salud e inteligencia artificial: ¿cómo hemos evolucionado?
Basáez, E., & Mora, J. (2022) · Revista Médica Clínica Las Condes · doi:10.1016/j.rmclc.2022.11.003 · Cited by 44
Li, Y., Wang, P., Feng, Q., Ji, X., Jin, D., & Gong, J. (2022) · Landslides · doi:10.1007/s10346-022-01997-2 · Cited by 30
Colorization of infrared images based on feature fusion and contrastive learning
Chen, L., Liu, Y., He, Y., Xie, Z., & Sui, X. (2022) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2022.107395 · Cited by 26
Harimi, A., Majd, Y., Gharahbagh, A. A., et al. (2022) · Sensors · doi:10.3390/s22249569 · Cited by 26
Big data and artificial intelligence application in energy field: a bibliometric analysis
Hou, Y., & Wang, Q. (2022) · Environmental Science and Pollution Research · doi:10.1007/s11356-022-24880-y · Cited by 28
Massimi, F., Ferrara, P., & Benedetto, F. (2022) · Sensors · doi:10.3390/s23010124 · Cited by 30
Land-Sea Target Detection and Recognition in SAR Image Based on Non-Local Channel Attention Network
Wang, Z., Xin, Z., Liao, G., Huang, P., Xuan, J., Sun, Y. S., & Tai, Y. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3217035 · Cited by 30
Input-level Inductive Biases for 3D Reconstruction
Wang, Y., Doersch, C., Arandjelović, R., Carreira, J., & Zisserman, A. (2022) · 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/cvpr52688.2022.00608 · Cited by 26
Time-rEversed DiffusioN tEnsor Transformer: A New TENET of Few-Shot Object Detection
Zhang, S., Murray, N., Wang, L., & Koniusz, P. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20044-1_18 · Cited by 29
OSANet: Object Semantic Attention Network for Visual Sentiment Analysis
Lee, S., Ryu, C., & Park, E. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3217414 · Cited by 27
Fine-Grained Image Classification by Class and Image-Specific Decomposition With Multiple Views
Zhang, C., Bai, H., & Zhao, Y. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3214431 · Cited by 27
Graph Convolutional Networks based on manifold learning for semi-supervised image classification
Valem, L. P., Pedronette, D. C. G., & Latecki, L. J. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103618 · Cited by 31
Animal image identification and classification using deep neural networks techniques
Battu, T., & Lakshmi, D. S. R. (2022) · Measurement Sensors · doi:10.1016/j.measen.2022.100611 · Cited by 26
A graphical approach for filter pruning by exploring the similarity relation between feature maps
Li, J., Shao, H., Zhai, S., Jiang, Y., & Deng, X. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.12.028 · Cited by 27
Advanced CNN Architectures for Pollen Classification: Design and Comprehensive Evaluation
Matavulj, P., Panić, M. N., Šikoparija, B., Tešendić, D., Radovanović, M., & Brdar, S. (2022) · Applied Artificial Intelligence · doi:10.1080/08839514.2022.2157593 · Cited by 26
Zhang, L., Leng, X., Feng, S., Ma, X., Ji, K., Kuang, G., & Liu, L. (2023) · Remote Sensing · doi:10.3390/rs15020331 · Cited by 27
Multi-Source Interactive Stair Attention for Remote Sensing Image Captioning
Zhang, X., Li, Y., Wang, X. E., Liu, F., Wu, Z., Cheng, X., & Jiao, L. (2023) · Remote Sensing · doi:10.3390/rs15030579 · Cited by 26
Latorre, F., Spiller, D., Sasidharan, S. T., Basheer, S., & Curti, F. (2023) · Icarus · doi:10.1016/j.icarus.2023.115434 · Cited by 27
Mencattini, A., Rizzuto, V., Antonelli, G., Giuseppe, D. D., D’Orazio, M., Filippi, J., Comes, M. C., Casti, P., Corrons, J. L. V., García-Bravo, M., et al. (2023) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2023.114187 · Cited by 27
A Survey on Training Issues in Chili Leaf Diseases Identification Using Deep Learning Techniques
Kanaparthi, K. R., & Ilango, S. S. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.01.188 · Cited by 26
Wang, D., Yang, R., Wang, X., Li, S., Tan, J., Zhang, S., Wei, S.-Y., Wu, Z.-Y., Chen, C., & Yang, X. (2023) · Journal of Mountain Science · doi:10.1007/s11629-022-7326-5 · Cited by 28
Chiaranai, S., Pitakaso, R., Sethanan, K., Kosacka-Olejnik, M., Srichok, T., & Chokanat, P. (2023) · Processes · doi:10.3390/pr11020434 · Cited by 26
A Dynamic Self-Attention-Based Fault Diagnosis Method for Belt Conveyor Idlers
Liu, Y., Miao, C., Li, X., Ji, J., Meng, D., & Wang, Y. (2023) · Machines · doi:10.3390/machines11020216 · Cited by 30
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 ›
Are you an author, or do you see a problem with this page? Request a review
