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 54,101–54,200 of 108,439
CSNet: a ConvNeXt-based Siamese network for RGB-D salient object detection
Zhang, Y., Wang, H., Yang, G., Zhang, J., Gong, C., & Wang, Y. (2023) · The Visual Computer · doi:10.1007/s00371-023-02887-x · Cited by 9
Sarkar, S., & Kelley, R. R. (2023) · IEEE Conference on Technologies for Sustainability · doi:10.1109/sustech57309.2023.10129545 · Cited by 9
Pavanello, F., Vatajelu, E. I., Bosio, A., Vaerenbergh, T. V., Bienstman, P., Charbonnier, B., Carpegna, A., Carlo, S. D., & Savino, A. (2023) · IEEE VLSI Test Symposium (VTS) · doi:10.1109/vts56346.2023.10139932 · Cited by 12
Traffic Sign Recognition System for Autonomous Vehicles using Deep Learning
Jency, S., Karthika, S., AjayKumar, J., et al. (2023) · International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei56765.2023.10125896 · Cited by 9
GUI Component Detection-Based Automated Software Crash Diagnosis
Nam, S.-G., & Seo, Y. (2023) · Electronics · doi:10.3390/electronics12112382 · Cited by 10
Chiu, P.-F., Chang, K., Lai, Y.-C., Wu, K.-C., Wang, K.-P., Chiu, Y.-P., Ji, H., Kao, C., & Chiu, C. (2023) · Diagnostics · doi:10.3390/diagnostics13111863 · Cited by 13
Zhou, K., Huang, X., Li, S., & Li, G. (2023) · Review of Scientific Instruments · doi:10.1063/5.0147783 · Cited by 10
Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation
Li, Z., Kamnitsas, K., Dou, Q., Qin, C., & Glocker, B. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3282728 · Cited by 10
Joint Diagnostic Method of Tumor Tissue Based on Hyperspectral Spectral-Spatial Transfer Features
Du, J. Q., Tao, C., Xue, S., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13122002 · Cited by 9
Hebscher, M., Bainbridge, W. A., & Voss, J. L. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120220 · Cited by 11
A Hyper-Network Based End-to-End Visual Servoing With Arbitrary Desired Poses
Yu, H., Chen, A., Xu, K., et al. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3288382 · Cited by 9
AI-ASSISTED DIGITALISATION OF HISTORICAL DOCUMENTS
Ferro, S., Pelillo, M., & Traviglia, A. (2023) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlviii-m-2-2023-557-2023 · Cited by 14
An AI-Based Early Fire Detection System Utilizing HD Cameras and Real-Time Image Analysis
Remmelzwaal, L. A. (2023) · Artificial Intelligence and Applications · doi:10.47852/bonviewaia3202975 · Cited by 9
SweepNet: A Lightweight CNN Architecture for the Classification of Adaptive Genomic Regions
Zhao, H., Pavlidis, P., & Alachiotis, N. (2023) · Platform for Advanced Scientific Computing Conference (PASC) · doi:10.1145/3592979.3593411 · Cited by 9
Water identification from the GF-1 satellite image based on the deep convolutional neural networks
Guojie, W. A. N. G., Yifan, H. U., Zhang, S., Yi, R., Kainan, C. H. E. N., & Mengjuan, W. U. (2022) · National Remote Sensing Bulletin · doi:10.11834/jrs.20210175 · Cited by 10
An embarrassingly simple approach for visual navigation of forest environments
Niu, C., Newlands, C., Zauner, K., & Tarapore, D. (2023) · Frontiers in Robotics and AI · doi:10.3389/frobt.2023.1086798 · Cited by 9
Deep learning-based gastric cancer diagnosis and clinical management
Xie, K., & Peng, J. (2023) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2023.100602 · Cited by 10
Simeuro: A Hybrid CPU-GPU Parallel Simulator for Neuromorphic Computing Chips
Zhang, H., Ho, N.-M., Polat, D. Y., et al. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3291795 · Cited by 9
Wang, Q., Lohse, J., Doulgeris, A. P., & Eltoft, T. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3291927 · Cited by 10
A Novel SAR Automatic Target Recognition Method Based on Fully Complex-Valued Networks
Zhu, Y., Li, T., Peng, D., et al. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3292315 · Cited by 9
Domain knowledge-guided adversarial adaptive fusion of hybrid breast ultrasound data
Gong, X., Yuan, S., Xiang, Y., Fan, L., & Zhou, H. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107256 · Cited by 9
Babalola, F. O., Toygar, Ö., & Bitirim, Y. (2023) · Signal Image and Video Processing · doi:10.1007/s11760-023-02671-3 · Cited by 9
Sparsity-Aware Orthogonal Initialization of Deep Neural Networks
Esguerra, K., Nasir, M., Tang, T. B., Tumian, A., & Ho, E. T. W. (2023) · IEEE Access · doi:10.1109/access.2023.3295344 · Cited by 9
Lendzioch, T., Langhammer, J., & Sheshadrivasan, V. K. (2023) · Journal of Hydroinformatics · doi:10.2166/hydro.2023.079 · Cited by 9
Skip-ST: Anomaly Detection for Medical Images Using Student-Teacher Network with Skip Connections
Liu, M., Jiao, Y., & Chen, H. (2023) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas46773.2023.10181639 · Cited by 10
A Novel Light U-Net Model for Left Ventricle Segmentation Using MRI
Irshad, M., Yasmin, M., Sharif, M. I., Rashid, M., Sharif, M. I., & Kadry, S. (2023) · Mathematics · doi:10.3390/math11143245 · Cited by 10
Surface defect classification of hot-rolled steel strip based on mixed attention mechanism
Fan, H., Dong, Q., & Guo, N. (2023) · Robotic Intelligence and Automation · doi:10.1108/ria-01-2023-0001 · Cited by 9
DLIRIR : Deep learning based improved Reverse Image Retrieval
Singh, D., Mathew, J., Agarwal, M., & Govind, M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106833 · Cited by 12
Zhou, Z., Yu, H., Zhao, J., et al. (2023) · Journal of Innovative Optical Health Sciences · doi:10.1142/s1793545823500190 · Cited by 9
Kong, X., Zhao, X., Sun, X., Wang, P., Wu, Y. N., Peng, R.-Y., Zhang, Q.-Y., Wang, Y., Li, R., Yang, Y., et al. (2023) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-023-00579-7 · Cited by 10
DPCNN-based Models for Text Classification
Zhang, M., Pang, J., Cai, J., Huo, Y., Yang, C., & Xiong, H. (2023) · IEEE International Conference on Cyber Security and Cloud Computing / IEEE International Conference on Edge Computing and Scalable Cloud (CSCloud/EdgeCom) · doi:10.1109/cscloud-edgecom58631.2023.00068 · Cited by 12
Liu, S., Zhang, Q., & Huang, L. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.07.027 · Cited by 9
A Novel Activation Function of Deep Neural Network
Lin, X., Xing, Q., Zhang, H., & Chen, F. (2023) · Scientific Programming · doi:10.1155/2023/3873561 · Cited by 12
Fairness via Group Contribution Matching
Li, T., Li, Z., Li, A., Du, M., Liu, A., Guo, Q., Meng, G., & Liu, Y. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/49 · Cited by 9
Best Practices for 2-Body Pose Forecasting
Rahman, M. R. U., Scofano, L., Matteis, E. D., Flaborea, A., Sampieri, A., & Galasso, F. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00369 · Cited by 9
Visualization and Classification of Radar Emitter Pulse Sequences Based on 2d Parameter Map
Wang, J., Wang, H., Xu, K., Mao, Y., Xuan, Z., Tang, B., Wang, X., & Mu, X. (2022) · Physical Communication · doi:10.1016/j.phycom.2023.102168 · Cited by 9
Towards Bridging the Performance Gaps of Joint Energy-Based Models
Yang, X., Su, Q. F., & Ji, S. (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/cvpr52729.2023.01510 · Cited by 11
Efficient Real-Time Recognition Model of Plant Diseases for Low-Power Consumption Platform
Deng, S., Wu, W., Zou, K., et al. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3307662 · Cited by 9
Sreedhar, S., Philip, A. O., & Sreeja, M. U. (2023) · International Journal of Information Technology · doi:10.1007/s41870-023-01415-3 · Cited by 9
Class-aware Variational Auto-encoder for Open Set Recognition
Wang, R., Guo, J., Zhao, R.-W., et al. (2023) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme55011.2023.00053 · Cited by 9
Investigating the other race effect: Human and computer face matching and similarity judgements
Ritchie, K. L., Cartledge, C., & Kramer, R. (2023) · Visual Cognition · doi:10.1080/13506285.2023.2250514 · Cited by 8
Grasp Detection Combining Self-Attention with CNN in Complex Scenes
Niu, J., Liu, S., Li, H., et al. (2023) · Applied Sciences · doi:10.3390/app13179655 · Cited by 9
Application of UAVs and Image Processing for Riverbank Inspection
Chiang, C.-H., & Juang, J. (2023) · Machines · doi:10.3390/machines11090876 · Cited by 9
Heterogeneous Ensemble for Medical Data Classification
Nanni, L., Brahnam, S., Loreggia, A., & Barcellona, L. (2023) · Analytics · doi:10.3390/analytics2030037 · Cited by 8
MeshCLIP: Efficient cross-modal information processing for 3D mesh data in zero/few-shot learning
Song, Y., Liang, N., Guo, Q., Dai, J., Bai, J., & He, F. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103497 · Cited by 9
Yan, H., Zhong, C., Wu, Y., et al. (2023) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2200515 · Cited by 9
Eldebiky, A., Zhang, G. L., Böcherer, G., Li, B., & Schlichtmann, U. (2023) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2023.3313089 · Cited by 11
Reddy, C. S., Park, E., & Lee, J. T. (2023) · IEEE Access · doi:10.1109/access.2023.3315342 · Cited by 10
Segmentation of MR Images using DN Convolutional Neural Network
Bhairnallykar, S. T., & Narawade, V. E. (2023) · International Journal of Information Technology · doi:10.1007/s41870-023-01461-x · Cited by 11
The gyrotron for DNP-NMR spectroscopy: A review
Nowak, K. (2022) · Bulletin of the Polish Academy of Sciences Technical Sciences · doi:10.24425/bpasts.2022.140354 · Cited by 9
Deep Learning Approach for the Localization and Analysis of Surface Plasmon Scattering
Lee, J., Moon, G., Ka, S., Toh, K., & Kim, D. (2023) · Sensors · doi:10.3390/s23198100 · Cited by 9
Russel, N. S., & Selvaraj, A. (2023) · The Visual Computer · doi:10.1007/s00371-023-03089-1 · Cited by 9
Singh, S. A., Devi, N. D., Singh, K. N., et al. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17186-9 · Cited by 9
Effective Brain Stroke Prediction with Deep Learning Model by Incorporating YOLO_5 and SSD
Sailaja, Y., & Pattani, V. (2023) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v19i14.41065 · Cited by 9
Biologically plausible local synaptic learning rules robustly implement deep supervised learning
Konishi, M., Igarashi, K. M., & Miura, K. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1160899 · Cited by 9
Revolutionizing Medical Imaging through Deep Learning Techniques:An Overview.
Elgayar, S., Hamad, S. H., & El-Horbaty, E.-S. M. (2023) · International journal of intelligent computing and information sciences/International Journal of Intelligent Computing and Information Sciences · doi:10.21608/ijicis.2023.211266.1274 · Cited by 9
Semisupervised Complex Network With Spatial Statistics Fusion for PolSAR Image Classification
Jiang, Y., Li, M., Zhang, P., et al. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3325365 · Cited by 9
Cross-device target migration intelligent diagnosis method of wind power gearbox faults
Guo, P., Cui, B., & Zhang, W. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/ad051d · Cited by 9
Deep Learning Performance Characterization on GPUs for Various Quantization Frameworks
Shafique, M. A., Munir, A., & Kong, J. (2023) · AI · doi:10.3390/ai4040047 · Cited by 14
Jiang, J. H., Chao, W., Cao, T., Culp, S., Napoléon, B., El-Dika, S. S., Machicado, J. D., Pannala, R., Mok, S. R. S., Luthra, A., et al. (2023) · Biomimetics · doi:10.3390/biomimetics8060496 · Cited by 10
Performance Comparison of Multispectral Channels for Land Use Classification
Nampally, T., Wu, J., & Dev, S. (2023) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss52108.2023.10283090 · Cited by 11
Li, L., Wang, L., Du, A., et al. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3326962 · Cited by 9
Cartoon-texture guided network for low-light image enhancement
Shi, B., Zhu, C., Li, L., et al. (2023) · Digital Signal Processing · doi:10.1016/j.dsp.2023.104271 · Cited by 9
Convergence Analysis of the Deep Galerkin Method for Weak Solutions
Jiao, Y., Lai, Y., Wang, Y., et al. (2023) · Applied and numerical harmonic analysis · doi:10.1007/978-3-031-37800-3_4 · Cited by 9
Shu, Y., Wang, W., Zhou, Y., Liu, S., Zhang, A., Yang, D., & Wang, W. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612383 · Cited by 9
HP-MIA: A novel membership inference attack scheme for high membership prediction precision
Chen, S., Wang, W., Zhong, Y., Ying, Z., Tang, W., & Pan, Z. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103571 · Cited by 10
Huang, S., Lee, C., Hsu, C., Chang, S.-Y., Chen, Y., Chiu, C., Hsiao, C.-C., & Su, H. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1253598 · Cited by 10
Deep learning in optics—a tutorial
Hadad, B., Froim, S., Yosef, E., Giryes, R., & Bahabad, A. (2023) · Journal of Optics · doi:10.1088/2040-8986/ad08dc · Cited by 10
Asiri, A. A. M., Aamir, M., Ali, T. M., et al. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1667 · Cited by 9
Ensemble of classifiers based on CNN for increasing generalization ability in face image recognition
Szmurło, R., & Osowski, S. (2022) · Bulletin of the Polish Academy of Sciences Technical Sciences · doi:10.24425/bpasts.2022.141004 · Cited by 9
Liu, Y., Pan, X., Liu, J., et al. (2023) · International Journal of Digital Earth · doi:10.1080/17538947.2023.2279671 · Cited by 9
Modeling the soil-machine response of secondary tillage: A deep learning approach
Boysen, J., Zender, L., & Stein, A. (2023) · Smart Agricultural Technology · doi:10.1016/j.atech.2023.100363 · Cited by 11
Hou, Z., Cai, K., Xiao, G., Fang, Q., Feng, Y., Chen, H., Yue, H., Wang, Z., & Lin, Q. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.108422 · Cited by 9
Sun, Y., Xie, J., Wang, Y., Chan, T. O., & Sun, Z. (2023) · Geo-spatial Information Science · doi:10.1080/10095020.2023.2273826 · Cited by 9
Chen, C.-Y., & Yu, T.-T. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.139737 · Cited by 11
Deep-Learning-Based Anti-Collision System for Construction Equipment Operators
Lee, Y., Kim, D.-K., & Kim, J. H. (2023) · Sustainability · doi:10.3390/su152316163 · Cited by 10
Skeletal age evaluation using hand X-rays to determine growth problems
Umer, M., Eshmawi, A. A., Alnowaiser, K., Mohamed, A., Alrashidi, H., & Ashraf, I. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1512 · Cited by 11
A Deep Convolutional Neural Network for Leaf Disease Detection of Sugarcane
Maurya, R., Sangaiah, A. K., & Singh, J. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10306751 · Cited by 9
Fast Prediction Method of Combustion Chamber Parameters Based on Artificial Neural Network
Shao, C., Liu, Y., Zhang, Z., Lei, F., & Fu, J. (2023) · Electronics · doi:10.3390/electronics12234774 · Cited by 10
Perspective and prospects of in situ transmission/scanning transmission electron microscopy
Sharma, R., & Yang, W.-C. D. (2023) · Microscopy · doi:10.1093/jmicro/dfad057 · Cited by 9
Image classification for historical documents: a study on Chinese local gazetteers
Chen, J.-A., Hou, J.-C., Tsai, R. T., Liao, H.-M., Chen, S.-P., & Chang, M. (2023) · Digital Scholarship in the Humanities · doi:10.1093/llc/fqad065 · Cited by 9
Advanced Hybrid Model for Multi Paddy diseases detection using Deep Learning
Dixit, A. K., & Verma, R. (2023) · EAI Endorsed Transactions on Pervasive Health and Technology · doi:10.4108/eetpht.9.4481 · Cited by 9
PLU‐Net: Extraction of multiscale feature fusion
Song, W., Yu, H., & Wu, J. (2023) · Medical Physics · doi:10.1002/mp.16840 · Cited by 9
Two-Stream Networks for COPERT Correction Model with Time-Frequency Features Fusion
Xu, Z., Wang, R., Pan, K., Li, J., & Wu, Q. (2023) · Atmosphere · doi:10.3390/atmos14121766 · Cited by 9
Emotion Recognition Using Wavelet Synchrosqueezing Transform Integrated With Ensemble Deep Learning
Kamble, K. S., & Sengupta, J. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3335229 · Cited by 9
Shen, C., Larson, J., Trinh, H., Qin, X., Park, Y., & Priebe, C. E. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3337600 · Cited by 12
Zhao, D., Shao, F., Yang, L., et al. (2023) · Electronics · doi:10.3390/electronics12234887 · Cited by 9
Deep learning-driven multi-view multi-task image quality assessment method for chest CT image
Su, J., Li, M., Lin, Y., Liu, X., Yuan, C., Zhou, Z., & Yan, K. (2023) · BioMedical Engineering OnLine · doi:10.1186/s12938-023-01183-y · Cited by 9
Online Continual Learning for Robust Indoor Object Recognition
Michieli, U., & Özay, M. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341474 · Cited by 10
CNN-based automated approach to crack-feature detection in steam cycle components
Fei, Z., West, G. M., Murray, P., & Dobie, G. (2023) · International Journal of Pressure Vessels and Piping · doi:10.1016/j.ijpvp.2023.105112 · Cited by 10
Liu, R., Wang, H., Xiao, H., Li, J., Pan, P., & Yang, W. (2023) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2023.134557 · Cited by 9
Bearing Fault Diagnosis Based on Deep Discriminative Adversarial Domain Adaptation Neural Networks
Guo, J., Chen, K., Liu, J., Ma, Y., Wu, J., Wu, Y., Xue, X., & Li, J. (2023) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.031360 · Cited by 12
Yang, F., Liu, J., Hua, C., Liu, W., & Dong, D. (2023) · Vehicle System Dynamics · doi:10.1080/00423114.2023.2295935 · Cited by 9
A new large dataset and a transfer learning methodology for plant phenotyping in Vertical Farms
Samà, N., David, É., Rossetti, S., Antona, A., Franchetti, B., & Pirri, F. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00061 · Cited by 11
Studying the Impact of Augmentations on Medical Confidence Calibration
Rao, A., Lee, J., & Aalami, O. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00260 · Cited by 10
A 9T-SRAM in-memory computing macro for Boolean logic and multiply-and-accumulate operations
Dai, C., Ren, Z., Guan, L., Liu, H., Gao, M., Lu, W., Pang, Z., Peng, C., & Wu, X. (2023) · Microelectronics Journal · doi:10.1016/j.mejo.2023.106087 · Cited by 9
BWLM: A Balanced Weight Learning Mechanism for Long-Tailed Image Recognition
Fan, B., Ma, H., Liu, Y., & Yuan, X. (2024) · Applied Sciences · doi:10.3390/app14010454 · Cited by 9
Deep learning-based modulation recognition with constellation diagram: A case study
Leblebici, M., Çalhan, A., & Ci̇ci̇oğlu, M. (2024) · Physical Communication · doi:10.1016/j.phycom.2024.102285 · Cited by 11
Lou, L., Song, M., Chen, X., et al. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3352561 · Cited by 9
Translating Images to Road Network: A Non-Autoregressive Sequence-to-Sequence Approach
Lu, J., Li, H., Peng, R., Wen, F., Cai, X., Zhang, W., Xu, H., & Zhang, L. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00009 · Cited by 13
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
