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 59,601–59,700 of 108,438
Convolutional Filter Approximation Using Fractional Calculus
Zamora, J., Vargas, J., Rhodes, A. D., Nachman, L., & Sundararajan, N. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00047 · Cited by 9
Detecting Extratropical Cyclones of the Northern Hemisphere with Single Shot Detector
Shi, M., He, P., & Shi, Y. (2022) · Remote Sensing · doi:10.3390/rs14020254 · Cited by 7
Usynin, D., Rueckert, D., Passerat‐Palmbach, J., & Kaissis, G. A. (2021) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2022-0014 · Cited by 10
Comparison of Backpropagation and Kalman Filter-based Training for Neural Networks
Luttmann, L., & Mercorelli, P. (2021) · International Conference on System Theory, Control and Computing (ICSTCC) · doi:10.1109/icstcc52150.2021.9607274 · Cited by 9
Pre-locate net for object detection in high-resolution images
ZHANG, Y., Xu, T.-B., & Wei, Z. (2021) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2021.10.022 · Cited by 7
A Survey on Graph Neural Networks for Recommendation
Liang, Z., Ding, H., & Fu, W. (2021) · International Conference on Culture-oriented Science & Technology (ICCST) · doi:10.1109/iccst53801.2021.00086 · Cited by 7
An Improved Efficient Convolutional Neural Network for Weed Seedlings Detection
Dou, M., Hong, Z., & Shi, M. (2021) · International Conference on Culture-oriented Science & Technology (ICCST) · doi:10.1109/iccst53801.2021.00067 · Cited by 7
Xing, X., Song, P., Zhang, K., et al. (2021) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc46164.2021.9630452 · Cited by 7
Facial Landmark Based Region of Interest Localization for Deep Facial Expression Recognition
Söylemez, Ö. F., & Ergen, B. (2021) · Tehnicki vjesnik - Technical Gazette · doi:10.17559/tv-20200423145443 · Cited by 7
Shahsenov, I., Malikov, R., Cook, P., et al. (2021) · Journal of Applied Geophysics · doi:10.1016/j.jappgeo.2021.104523 · Cited by 7
Optimal CNN–Hopfield Network for Pattern Recognition Based on a Genetic Algorithm
Keddous, F. E., & Nakib, A. (2021) · Algorithms · doi:10.3390/a15010011 · Cited by 7
Discriminative Localized Sparse Approximations for Mass Characterization in Mammograms
Makrogiannis, S. K., Zheng, K., & Harris, C. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.725320 · Cited by 7
Cao, F., Pu, T., & Xie, C. (2021) · Applied Optics · doi:10.1364/ao.444798 · Cited by 8
Deep Transfer Learning for Biology Cross-Domain Image Classification
Guo, C., Wei, B., & Yu, K. (2021) · Journal of Control Science and Engineering · doi:10.1155/2021/2518837 · Cited by 7
One-Class Model for Fabric Defect Detection
Zhou, H. Y., Chen, Y., Troendle, D., & Jang, B. (2021) · Natural Language Processing · doi:10.5121/csit.2021.112314 · Cited by 9
Incremental Learning of Object Detector with Limited Training Data
Hafeez, M. A., Ul-Hasan, A., & Faisal Shafait, B. (2021) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta52665.2021.9647245 · Cited by 7
FedDCS: Federated Learning Framework based on Dynamic Client Selection
Zou, S., Xiao, M., Xu, Y., An, B., & Zheng, J. (2021) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass52906.2021.00090 · Cited by 9
Dual-stream collaborative tracking algorithm combined with reliable memory based update
Zhang, Y., Liu, G., Huang, H., Xiong, R., & Zhang, H. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.01.046 · Cited by 8
Deep learning based camera and radar fusion for object detection and classification
Abdalwohab, M., Zhang, W., Abdelgader, A. M. S., et al. (2021) · IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) · doi:10.1109/auteee52864.2021.9668695 · Cited by 7
Pest and Disease Detection from Plant Leaves using Enhanced AlexNet Model
Sameer, S. M., Niharika, B., Vasavi, S., et al. (2021) · IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · doi:10.1109/conecct52877.2021.9622569 · Cited by 7
UGCLink: User Identity Linkage by Modeling User Generated Contents with Knowledge Distillation
Gao, H., Wang, Y., Shao, J., et al. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671907 · Cited by 7
A Reversible-Logic based Architecture for VGGNet
Dey, B., Khalil, K., Kumar, A., & Bayoumi, M. (2021) · IEEE International Conference on Electronics, Circuits and Systems (ICECS) · doi:10.1109/icecs53924.2021.9665605 · Cited by 7
Arithmetic Coding-Based 5-Bit Weight Encoding and Hardware Decoder for CNN Inference in Edge Devices
Lee, J. H., Kong, J., & Munir, A. (2021) · IEEE Access · doi:10.1109/access.2021.3136888 · Cited by 8
Reduced-Lead Electrocardiogram Classification Using Wavelet Analysis and Deep Learning
Cornely, A. K., Carrillo, A., & Mirsky, G. (2021) · Computing in cardiology · doi:10.23919/cinc53138.2021.9662813 · Cited by 7
RCGA-Net: An Improved Multi-hybrid Attention Mechanism Network in Biomedical Image Segmentation
Xiao, F., Shen, C., Chen, Y., et al. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669413 · Cited by 7
Application of Artificial Intelligence in Cardiovascular Imaging
Ma, P., Li, Q., & Li, J. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/7988880 · Cited by 7
RetractedThe need for a numeric measure of explainability
Mulder, W. D., & Valcke, P. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671745 · Cited by 6
A review on machine learning techniques for acute leukemia classification
Rosales-Pérez, A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-820125-1.00033-6 · Cited by 8
Kortum, H., Rebstadt, J., & Thomas, O. (2022) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2022.635 · Cited by 8
Tian, Y., Sun, S., Qi, Z., Liu, Y., & Wang, Z. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.01.011 · Cited by 8
Ogawa, N., Maeda, K., Ogawa, T., & Haseyama, M. (2022) · Sensors · doi:10.3390/s22010382 · Cited by 7
Medicinal Plant Identification using Gabor Filters and Deep Learning Techniques: A Paper Review
Oppong, S. O., Twum, F., Hayfron-Acquah, J. B., & Missah, Y. M. (2021) · Journal of Computer Science · doi:10.3844/jcssp.2021.1210.1221 · Cited by 7
Detection and Classification of Human Stool Using Deep Convolutional Neural Networks
Choy, Y. P., Hu, G., & Chen, J. (2021) · IEEE Access · doi:10.1109/access.2021.3131982 · Cited by 8
Superclass-aware network for few-shot learning
Wu, S., Kankanhalli, M., & Tung, A. K. H. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103349 · Cited by 7
Research on Vision of Intelligent Car Based on Broad Learning System
Liu, X., & Wu, Y. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3137801 · Cited by 8
PRGAN: Personalized Recommendation with Conditional Generative Adversarial Networks
Wen, J., Chen, B., Wang, C., & Tian, Z. (2021) · Proceedings · doi:10.1109/icdm51629.2021.00084 · Cited by 8
Neural Network Verification using Polynomial Optimisation
Newton, M., & Papachristodoulou, A. (2021) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc45484.2021.9683286 · Cited by 7
Improving Fractional Vegetation Cover Estimation With Shadow Effects Using High Dynamic Range Images
Chen, W., Wang, Z., Zhang, X., et al. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3148282 · Cited by 7
Liu, C., Guo, Y., Jiang, F., et al. (2022) · Technology and Health Care · doi:10.3233/thc-228005 · Cited by 7
Towards Addressing the Spatial Sparsity of MDT Reports to Enable Zero Touch Network Automation
Shodamola, J., Qureshi, H. N., Masood, U., et al. (2021) · Globecom · doi:10.1109/globecom46510.2021.9686011 · Cited by 7
Deep Learning on Small Tabular Dataset: Using Transfer Learning and Image Classification
Jain, V., Goel, M., & Shah, K. (2022) · Communications in computer and information science · doi:10.1007/978-3-030-95711-7_46 · Cited by 7
An Effective Multi-Scale Feature Network for Detecting Connector Solder Joint Defects
Zhang, K., & Shen, H. (2022) · Machines · doi:10.3390/machines10020094 · Cited by 7
Neural network classifier of hyperspectral images of skin pathologies
Vinokurov, V., Матвеева, И. А., Khristoforova, Y. А., Myakinin, O. O., Bratchenko, I. А., Bratchenko, L. A., Moryatov, A. A., Kozlov, S. G., Machikhin, A. S., Abdulhalim, I. S., et al. (2021) · Computer Optics · doi:10.18287/2412-6179-co-832 · Cited by 7
Hand gesture recognition based improved multi-channels CNN architecture using EMG sensors
Li, J., Wei, L., Wen, Y., Liu, X., & Wang, H. (2022) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-212390 · Cited by 7
Deep Learning Technique for Fingerprint Recognition
Aboalhsan, A., & Alatawi, M. N. (2022) · International Conference on Computing and Information Technology (ICCIT) · doi:10.1109/iccit52419.2022.9711634 · Cited by 7
Multi-view 3D model retrieval based on enhanced detail features with contrastive center loss
Chen, Q., & Chen, Y. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12281-9 · Cited by 7
MisConv: Convolutional Neural Networks for Missing Data
Przewięźlikowski, M., Śmieja, M., Struski, Ł., et al. (2022) · IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00297 · Cited by 7
VLSI implementation using fully connected neural networks for energy consumption over neurons
Mehbodniya, A., Kumar, R., Bedi, P., Mohanty, S. N., Tripathi, R., & Geetha, A. (2022) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2022.102058 · Cited by 7
Clothing recognition based on deep sparse convolutional neural network
Xiang, J., Pan, R., & Gao, W. (2022) · International Journal of Clothing Science and Technology · doi:10.1108/ijcst-06-2018-0081 · Cited by 9
An Artificial Intelligence (AI)-Based Decision-Making Framework for Crisis Management
Essien, A. E., & Petrounias, I. (2022) · Advances in electronic government, digital divide, and regional development book series · doi:10.4018/978-1-7998-9815-3.ch007 · Cited by 9
Faster MDNet for Visual Object Tracking
Yu, Q., Fan, K., Wang, Y., et al. (2022) · Applied Sciences · doi:10.3390/app12052336 · Cited by 7
An Artificial Intelligence (AI) Approach to Controlling Disaster Scenarios
Schofield, M. (2022) · Advances in electronic government, digital divide, and regional development book series · doi:10.4018/978-1-7998-9815-3.ch003 · Cited by 8
Multistream 3-D Convolution Neural Network With Parameter Sharing for Human State Estimation
Lin, C., Liu, J., Fang, C.-N., Hsiao, S.-Y., Chang, Y., & Wang, Y. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2022.3153676 · Cited by 8
Robust Decision-Making in the Internet of Battlefield Things Using Bayesian Neural Networks
Cobb, A. D., Jalaian, B. A., Bastian, N. D., & Russell, S. (2021) · Winter Simulation Conference · doi:10.1109/wsc52266.2021.9715532 · Cited by 8
Multilabel Convolutional Network With Feature Denoising and Details Supplement
Gu, T., Wang, Z., Fang, Z., Zhu, Z., Yang, H., Li, D., & Du, W. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3149760 · Cited by 7
Long-term image-based vehicle localization improved with learnt semantic descriptors
Çınaroğlu, İ., & Baştanlar, Y. (2022) · Engineering Science and Technology an International Journal · doi:10.1016/j.jestch.2022.101098 · Cited by 7
Deep transfer learning CNN based approach for COVID-19 detection
Muhammad, W. (2022) · International Journal of ADVANCED AND APPLIED SCIENCES · doi:10.21833/ijaas.2022.04.006 · Cited by 8
Yang, H., Dong, B., Gu, W., Wu, S., Zhou, W., Zhang, X., & Wang, D. (2022) · Applied Radiation and Isotopes · doi:10.1016/j.apradiso.2022.110172 · Cited by 8
Face recognition in a large dataset using a hierarchical classifier
Abbaspoor, N., & Hassanpour, H. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12382-5 · Cited by 7
Capsule–Encoder–Decoder: A Method for Generalizable Building Extraction from Remote Sensing Images
Tang, Z., Chen, C. Y., Jiang, C., Zhang, D., Luo, W., Hong, Z., & Sun, H. (2022) · Remote Sensing · doi:10.3390/rs14051235 · Cited by 7
A Carbonate Reservoir Prediction Method Based on Deep Learning and Multiparameter Joint Inversion
Tian, X., Huang, H., Cheng, S., Wang, C., Li, P., & Hao, Y. (2022) · Energies · doi:10.3390/en15072506 · Cited by 8
Wu, K., Duan, S., Wang, Y., Wang, H., & Gao, X. (2022) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-022-02523-1 · Cited by 7
Rixen, J., Eliasson, B., Hentze, B., Muders, T., Putensen, C., Leonhardt, S. K., & Ngo, C. (2022) · Diagnostics · doi:10.3390/diagnostics12040777 · Cited by 7
Dual Modality Collaborative Learning for Cross-Source Remote Sensing Retrieval
Ma, J., Shi, D., Tang, X., Zhang, X., & Jiao, L. (2022) · Remote Sensing · doi:10.3390/rs14061319 · Cited by 7
Deep Learning Based Granularity Detection Network for Mine Dump Materials
Cai, Z., Lei, S., & Lu, X. (2022) · Minerals · doi:10.3390/min12040424 · Cited by 7
A feature selection method based on auto-encoder for internet of things intrusion detection
Alshudukhi, A. F., Jabbar, S. A., & Alshaikhdeeb, B. (2022) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v12i3.pp3265-3275 · Cited by 7
Jia, Y., Liu, L., Peng, S., Feng, M., & Wan, G. (2022) · Remote Sensing · doi:10.3390/rs14061391 · Cited by 8
Mining semantic information from intra-image and cross-image for few-shot segmentation
Liu, Y., Guo, Y., Zhu, Y. H., & Yu, M. Z. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12096-8 · Cited by 7
Morshed, M. G., & Lee, Y.-K. (2022) · IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp54360.2022.00055 · Cited by 7
Exploiting Graph and Geodesic Distance Constraint for Deep Learning-Based Visual Odometry
Xu, F., Li, Q., Li, Q., Ding, K., & Zhu, J. (2022) · Remote Sensing · doi:10.3390/rs14081854 · Cited by 8
SiamCAM: A Real-Time Siamese Network for Object Tracking with Compensating Attention Mechanism
Huang, K., Qin, P., Tu, X., Leng, L., & Chu, J. (2022) · Applied Sciences · doi:10.3390/app12083931 · Cited by 7
Stinner, F., Llopis-Mengual, B., Storek, T., Kümpel, A., & Müller, D. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104248 · Cited by 7
Fast CP‐compression layer: Tensor CP‐decomposition to compress layers in deep learning
Ji, Y., & Wang, Q. (2022) · IET Image Processing · doi:10.1049/ipr2.12507 · Cited by 7
A Deep Learning Method for DOA Estimation with Covariance Matrices in Reverberant Environments
Huang, Q., & Fang, W. (2022) · Applied Sciences · doi:10.3390/app12094278 · Cited by 7
Object Detection by Attention-Guided Feature Fusion Network
Shi, Y., Fan, Y. B., Xu, S., Gao, Y., & Gao, R. (2022) · Symmetry · doi:10.3390/sym14050887 · Cited by 7
Landslide Hazard Identification Based on Deep Learning and Sentinel-2 Remote Sensing Imagery
Xia, J., Liu, H., & Zhu, L. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2258/1/012031 · Cited by 7
The Sjtu System For Multimodal Information Based Speech Processing Challenge 2021
Wang, W., Gong, X., Wu, Y., et al. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746874 · Cited by 7
MsfNet: a novel small object detection based on multi-scale feature fusion
Song, Z., Wu, P., Yang, K., Zhang, Y., & Liu, Y. (2021) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn53354.2021.00108 · Cited by 8
Osanlou, K., Frank, J., Bursuc, A., Cazenave, T., Jacopin, É., Guettier, C., & Benton, J. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i9.21224 · Cited by 7
Cao, Y., Sui, B., & Zhang, W. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3166927 · Cited by 8
Applications of Hand Gesture Recognition
Sharma, H. K., & Choudhury, T. (2022) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-9434-6.ch010 · Cited by 7
STSM: Spatio-Temporal Shift Module for Efficient Action Recognition
Yang, Z., An, G., & Zhang, R. (2022) · Mathematics · doi:10.3390/math10183290 · Cited by 7
Sasilatha, T., Amala, D. H. M. S., Santhammal, R. S., et al. (2022) · Zastita materijala · doi:10.5937/zasmat2201005r · Cited by 7
Fine-grained classification of grape leaves via a pyramid residual convolution neural network
Li, H., Wei, Y., Zhang, H., et al. (2022) · International journal of agricultural and biological engineering · doi:10.25165/j.ijabe.20221502.6894 · Cited by 7
An Ensemble Learning Method of Adaptive Structural Deep Belief Network for AffectNet
Ichimura, T., & Kamada, S. (2022) · International Journal of Smart Computing and Artificial Intelligence · doi:10.52731/ijscai.v6.i1.640 · Cited by 7
Multi-Teacher Knowledge Distillation for Incremental Implicitly-Refined Classification
Yu, L., Weng, Z., Wang, Y., & Zhu, Y. (2022) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme52920.2022.9859936 · Cited by 9
Breast Cancer Detection in Saudi Arabian Women Using Hybrid Machine Learning on Mammographic Images
Almalki, Y. E., Shaf, A., Ali, T. M., Aamir, M., Alduraibi, S. K., Almutiri, S. M., Irfan, M., Basha, M. A. A., Alduraibi, A. K., Alamri, A. M., et al. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.027111 · Cited by 9
Hashim, B. A. M., & Amutha, R. (2022) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-213174 · Cited by 8
Capsule Embedded ResNet for Image Classification
Liu, W., Chen, W., Wang, C., et al. (2021) · doi:10.1145/3507548.3507570 · Cited by 7
Deep Learning on Side-Channel Analysis
Krček, M., Li, H., Paguada, S., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-98795-4_3 · Cited by 7
Cabrera, J. J., Cebollada, S., Flores, M., Reinoso, Ó., & Payá, L. (2022) · SN Computer Science · doi:10.1007/s42979-022-01127-8 · Cited by 7
Data-Rich Spatial Profiling of Cancer Tissue: Astronomy Informs Pathology
Szalay, A. S., & Taube, J. M. (2022) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-3748 · Cited by 7
Qian, H., Wang, X., Chen, X., et al. (2022) · Energies · doi:10.3390/en15093362 · Cited by 7
Deep neural network compression via knowledge distillation for embedded applications
Jaiswal, B., & Gajjar, N. (2017) · Nirma University International Conference on Engineering · doi:10.1109/nuicone.2017.8325620 · Cited by 9
Trajectory Analysis Method Based on Video Surveillance Anomaly Detection
Zhou, X., Chen, Y., & Zhang, Q. (2021) · China Automation Congress (CAC) · doi:10.1109/cac53003.2021.9727735 · Cited by 7
Bandwidth-Efficient Sparse Matrix Multiplier Architecture for Deep Neural Networks on FPGA
Mahesh, M., Nalesh, S., & Kala, S. (2021) · IEEE International System-on-Chip Conference (SOCC) · doi:10.1109/socc52499.2021.9739346 · Cited by 7
Nonhypothesis-Driven Research: Data Mining and Knowledge Discovery
Cummins, M. R. (2019) · Health informatics · doi:10.1007/978-3-319-98779-8_16 · Cited by 7
Han, X., Zhong, Y., & Zhang, L. (2016) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsannals-iii-7-25-2016 · Cited by 10
Carbonate Lithology Identification with Generative Adversarial Networks
Nanjo, T., & Tanaka, S. (2020) · International Petroleum Technology Conference · doi:10.2523/iptc-20226-abstract · Cited by 7
Architecture self-attention mechanism: nonlinear optimization for neural architecture search
(2021) · Journal of Nonlinear and Variational Analysis · doi:10.23952/jnva.5.2021.1.08 · Cited by 7
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
