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 36,301–36,400 of 108,435
Deformable Cardiovascular Image Registration via Multi-Channel Convolutional Neural Network
Guan, S., Cai, M., Xie, Y., Wang, Q., Sun, K., & Wang, T. (2019) · IEEE Access · doi:10.1109/access.2019.2894943 · Cited by 21
Quantifying lung ultrasound comets with a convolutional neural network: Initial clinical results
Wang, X., Burzynski, J. S., Hamilton, J. D., et al. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.02.002 · Cited by 21
Pu, Q., Li, Y., Zhang, H., Yao, H., Zhang, B., Hou, B., Li, L., Zhao, Y., & Zhao, L. (2019) · Science China Chemistry · doi:10.1007/s11426-018-9412-6 · Cited by 21
Actor-Critic-Based Resource Allocation for Multi-Modal Optical Networks
Yan, B., Zhao, Y., Li, Y., Yu, X., Zhang, J., Wang, Y., Yan, L., & Rahman, S. (2018) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/glocomw.2018.8644190 · Cited by 21
A Wireless Smart Camera Network for Parking Monitoring
Amato, G. M., Bolettieri, P., Moroni, D., Carrara, F., Ciampi, L., Pieri, G., Gennaro, C., Leone, G. R., & Vairo, C. (2018) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/glocomw.2018.8644226 · Cited by 27
Automatic Classification of Radiological Report for Intracranial Hemorrhage
Jnawali, K., Arbabshirani, M. R., Ulloa, A., Rao, N. A. H. K., & Patel, A. A. (2019) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icosc.2019.8665578 · Cited by 21
Combination and application of machine learning and computational mechanics
Li, X., Yan, Z., & Liu, Z. (2019) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/n972019-00005 · Cited by 20
Kamath, U., Liu, J., & Whitaker, J. (2019) · Springer eBooks · doi:10.1007/978-3-030-14596-5_6 · Cited by 23
Deep built-structure counting in satellite imagery using attention based re-weighting
Shakeel, A., Sultani, W., & Ali, M. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.03.014 · Cited by 20
GQ-STN: Optimizing One-Shot Grasp Detection based on Robustness Classifier
Gariépy, A., Ruel, J.-C., Chaib-draa, B., & Giguère, P. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967785 · Cited by 23
Flow Adaptive Video Object Segmentation
Lin, F., Chou, Y., & Martinez, T. R. (2019) · Image and Vision Computing · doi:10.1016/j.imavis.2019.103864 · Cited by 19
Advanced Neural Network Approach, Its Explanation with LIME for Credit Scoring Application
Munkhdalai, L., Wang, L., Park, H. W., & Ryu, K. H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-14802-7_35 · Cited by 26
Neural Network-Based Learning from Demonstration of an Autonomous Ground Robot
Fu, Y., Jha, D. K., Zhang, Z., Yuan, Z., & Ray, A. (2019) · Machines · doi:10.3390/machines7020024 · Cited by 22
Lung adenocarcinoma diagnosis in one stage
Hao, P., Kun, Y., Feng, H., et al. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.11.110 · Cited by 21
3D Facial Landmark Detection Using Deep Convolutional Neural Networks
Terada, T., Chen, Y., & Kimura, R. (2018) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/fskd.2018.8687254 · Cited by 21
The Effect of Color Channel Representations on the Transferability of Convolutional Neural Networks
Díaz-Cely, J., Arce‐Lopera, C., Mena, J. C., & Quintero, L. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-17795-9_3 · Cited by 22
Indonesian Herbs and Spices Recognition using Smaller VGGNet-like Network
Khrisne, D. C., & Suyadnya, I. M. A. (2018) · International Conference on Smart-Green Technology in Electrical and Information Systems (ICSGTEIS) · doi:10.1109/icsgteis.2018.8709135 · Cited by 21
Samiee, A., Borulkar, P., DeMara, R. F., Zhao, P., & Bai, Y. D. (2019) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2019.2915589 · Cited by 22
Qiu, J., Wang, X., Maybank, S. J., & Tao, D. (2019) · 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/cvpr.2019.00869 · Cited by 20
Convolution Accelerator Designs Using Fast Algorithms
Zhao, Y., Wang, D., & Wang, L. (2019) · Algorithms · doi:10.3390/a12050112 · Cited by 22
Chen, L., Cui, X., Li, Z., Yuan, Z., Jin, X., Xing, X., & Jia, Z. (2019) · Sensors · doi:10.3390/s19112479 · Cited by 22
3D shape segmentation via shape fully convolutional networks
Wang, P., Gan, Y., Shui, P., Yu, F., Zhang, Y., Chen, S., & Sun, Z. (2018) · Computers & Graphics · doi:10.1016/j.cag.2018.07.011 · Cited by 22
Computer Vision System for Automatic Counting of Planting Microsites Using UAV Imagery
Bouachir, W., Ihou, K. E., Gueziri, H.-E., Bouguila, N., & Bélanger, N. (2019) · IEEE Access · doi:10.1109/access.2019.2923765 · Cited by 22
Neural fashion experts: I know how to make the complementary clothing matching
Liu, J., Song, X., Chen, Z., & Ma, J. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.05.081 · Cited by 22
Accurate Fault Location Using Deep Belief Network for Optical Fronthaul Networks in 5G and Beyond
Yu, A., Yang, H., Yao, Q., Li, Y., Guo, H., Peng, T., Li, H., & Zhang, J. (2019) · IEEE Access · doi:10.1109/access.2019.2921329 · Cited by 21
Motorway Traffic Flow Prediction using Advanced Deep Learning
Mihăiţă, A., Li, H., He, Z., et al. (2019) · IEEE Intelligent Transportation Systems Conference (ITSC) · doi:10.1109/itsc.2019.8916852 · Cited by 21
Parallel Architecture of Fully Convolved Neural Network for Retinal Vessel Segmentation
Sathananthavathi, V., Indumathi, G., & Ranjani, A. S. (2019) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-019-00250-y · Cited by 21
Recent Progress of Face Image Synthesis
Lu, Z., Li, Z.-H., Cao, J., He, R., & Sun, Z.-N. (2017) · Asian Conference on Pattern Recognition · doi:10.1109/acpr.2017.2 · Cited by 35
An Enhanced Optimization Scheme Based on Gradient Descent Methods for Machine Learning
Yi, D., Ji, S., & Bu, S. (2019) · Symmetry · doi:10.3390/sym11070942 · Cited by 21
An ELU Network with Total Variation for Image Denoising
Wang, T., Qin, Z., & Zhu, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70090-8_24 · Cited by 25
Accelerating Deep Learning with Shrinkage and Recall
Zheng, S., Vishnu, A., & Ding, C. H. Q. (2016) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads.2016.0129 · Cited by 24
Tung, F., Muralidharan, S., & Mori, G. (2017) · British Machine Vision Conference (BMVC) · doi:10.5244/c.31.115 · Cited by 36
Interpreting Fine-Grained Dermatological Classification by Deep Learning
Mishra, S., Imaizumi, H., & Yamasaki, T. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00331 · Cited by 22
Small Object Detection Using Context Information Fusion in Faster R-CNN
Fang, P., & Shi, Y. (2018) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/compcomm.2018.8780579 · Cited by 21
Learning Shared Knowledge for Deep Lifelong Learning using Deconvolutional Networks
Lee, S., STOKES, J. M., & Eaton, E. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/393 · Cited by 20
DeepCEP: Deep Complex Event Processing Using Distributed Multimodal Information
Xing, T., Vilamala, M. R., García, L. A. R., Cerutti, F., Kaplan, L., Preece, A. D., & Srivastava, M. B. (2019) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp.2019.00034 · Cited by 26
Linear Registration of Brain MRI Using Knowledge-Based Multiple Intermediator Libraries
Zhang, X., Feng, Y., Chen, W., Li, X., Faria, A. V., Feng, Q., & Mori, S. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00909 · Cited by 23
Understanding community structure in layered neural networks
Watanabe, C., Hiramatsu, K., & Kashino, K. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.08.020 · Cited by 24
Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem
Tennakoon, R., Bortsova, G., Ørting, S. N., Gostar, A. K., Wille, M. M. W., Saghir, Z., Hoseinnezhad, R., de Bruijne, M., & Bab‐Hadiashar, A. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2936244 · Cited by 20
Broad Area Target Search System for Ship Detection via Deep Convolutional Neural Network
You, Y., Li, Z., Ran, B., Cao, J., Lv, S., & Liu, F. (2019) · Remote Sensing · doi:10.3390/rs11171965 · Cited by 21
DispVoxNets: Non-Rigid Point Set Alignment with Supervised Learning Proxies
Shimada, S., Golyanik, V., Tretschk, E., Stricker, D., & Theobalt, C. (2019) · International Conference on 3D Vision · doi:10.1109/3dv.2019.00013 · Cited by 29
Person Detection in Thermal Videos Using YOLO
Ivašić-Kos, M., Krišto, M., & Pobar, M. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-29513-4_18 · Cited by 21
Intelligent Health Vessel ABC-DE: An Electrocardiogram Cloud Computing Service
Jin, L., & Dong, J. (2018) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2018.2825390 · Cited by 21
Deep Representation Learning for Complex Free-Energy Landscapes
Zhang, J., Lei, Y.-K., Che, X., Zhang, Z., Yang, Y., & Gao, Y. Q. (2019) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.9b02012 · Cited by 21
Poon, H.-K., Yap, W., Tee, Y. K., Lee, W., & Goi, B. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.08.017 · Cited by 21
CNN and KPCA-Based Automated Feature Extraction for Real Time Driving Pattern Recognition
Xie, L., Tao, J., Zhang, Q., & Zhou, H. (2019) · IEEE Access · doi:10.1109/access.2019.2938768 · Cited by 21
Deep learning models for population flow generation from aggregated mobility data
Rong, C., Feng, J., & Li, Y. (2019) · Adjunct ACM International Joint Conference on Pervasive and Ubiquitous Computing and ACM International Symposium on Wearable Computers (UbiComp/ISWC) · doi:10.1145/3341162.3349319 · Cited by 21
Amorin, C. J., Kegelmeyer, L. M., & Kegelmeyer, W. P. (2019) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11437 · Cited by 21
Cellular Structure Image Classification With Small Targeted Training Samples
Wang, D., Lu, Z., Xu, Y., Wang, Z., Li, C., Santella, A., & Bao, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2940161 · Cited by 21
Automated detection of block falls in the north polar region of Mars
Fanara, L., Gwinner, K., Hauber, E., et al. (2019) · Planetary and Space Science · doi:10.1016/j.pss.2019.104733 · Cited by 21
Zhang, J., Li, X., Li, Y., Wang, M., Huang, B., Yao, S., & Shen, L. (2019) · Brain Imaging and Behavior · doi:10.1007/s11682-019-00186-5 · Cited by 21
MACHINE INTELLIGENCE. ESSAYS ON THE THEORY OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE
Шумский, С., & Shumskiy, S. (2019) · Publishing Center RIOR eBooks · doi:10.29039/02011-1 · Cited by 22
Investigation of machine learning techniques on proteomics: A comprehensive survey
Sonsare, P. M., & Gunavathi, C. (2019) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2019.09.004 · Cited by 23
Unsupervised selective rank fusion for image retrieval tasks
Valem, L. P., & Pedronette, D. C. G. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.09.065 · Cited by 21
Quantum-Inspired Neural Architecture Search
Szwarcman, D., Civitarese, D., & Vellasco, M. M. B. R. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852453 · Cited by 24
An LSTM based Encoder-Decoder Model for MultiStep Traffic Flow Prediction
Du, S., Li, T., Yang, Y. Z., Gong, X., & Horng, S. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851928 · Cited by 24
Using a Reinforcement Q-Learning-Based Deep Neural Network for Playing Video Games
Lin, C., Jhang, J., Lin, H.-Y., Lee, C., & Young, K. (2019) · Electronics · doi:10.3390/electronics8101128 · Cited by 21
Change Detection in SAR Images Based on Deep Learning
Keshk, H. M., & Yin, X.-C. (2019) · International Journal of Aeronautical and Space Sciences · doi:10.1007/s42405-019-00222-0 · Cited by 23
Privacy-Constrained Biometric System for Non-Cooperative Users
Jahromi, M. N. S., Buch-Cardona, P., Avots, E., et al. (2019) · Entropy · doi:10.3390/e21111033 · Cited by 21
LUNA: Lightweight UAV Navigation Based on Airborne Vision for Disaster Management
Xu, J., Ota, K., & Dong, M. (2019) · doi:10.1109/ithings/greencom/cpscom/smartdata.2019.00073 · Cited by 21
Urban acoustic classification based on deep feature transfer learning
Shen, Y., Cao, J., Wang, J., & Yang, Z.-X. (2019) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2019.10.014 · Cited by 22
Transfer Learning for Nonrigid 2D/3D Cardiovascular Images Registration
Guan, S., Wang, T., Sun, K., & Cai, M. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3045977 · Cited by 22
Solving Poisson's Equation using Deep Learning in Particle Simulation of PN Junction
Zhang, Z., Zhang, L., Sun, Z., et al. (2019) · doi:10.23919/emctokyo.2019.8893758 · Cited by 21
Pulmonary Textures Classification via a Multi-Scale Attention Network
Xu, R., Cong, Z., Ye, X., Hirano, Y., Kido, S., Gyobu, T., Kawata, Y., Honda, O., & Tomiyama, N. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2950006 · Cited by 21
Heo, J., Lee, J. J., Kwon, S., Kim, B., Hwang, S. O., & Yoon, Y. R. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101700 · Cited by 22
Research on application of athlete gesture tracking algorithms based on deep learning
Long, T. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01575-w · Cited by 21
Brinke, J. K., & Meratnia, N. (2019) · doi:10.1145/3363347.3363362 · Cited by 21
Mtonga, K., Kumaran, S., Mikeka, C., Jayavel, K., & Nsenga, J. (2019) · Future Internet · doi:10.3390/fi11110236 · Cited by 23
Self-Supervised Contextual Data Augmentation for Natural Language Processing
Park, D., & Ahn, C. W. (2019) · Symmetry · doi:10.3390/sym11111393 · Cited by 22
FPGA-Based Training Accelerator Utilizing Sparseness of Convolutional Neural Network
Nakahara, H., Sada, Y., Shimoda, M., Sayama, K., Jinguji, A., & Sato, S. (2019) · International Conference on Field Programmable Logic and Applications (FPL) · doi:10.1109/fpl.2019.00036 · Cited by 23
Person Re-Identification With Deep Kronecker-Product Matching and Group-Shuffling Random Walk
Shen, Y., Xiao, T., Yi, S., Chen, D., Wang, X., & Li, H. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2954313 · Cited by 21
Deep person re-identification in UAV images
Grigorev, A., Tian, Z., Rho, S., Xiong, J., Liu, S., & Jiang, F. (2019) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-019-0647-z · Cited by 23
Hyperspectral Image Denoising With Dual Deep CNN
Shan, W., Liu, P., Mu, L., Cao, C., & He, G. (2019) · IEEE Access · doi:10.1109/access.2019.2955810 · Cited by 22
AOP: An Anti-overfitting Pretreatment for Practical Image-based Plant Diagnosis
Saikawa, T., Huu, Q., Kagiwada, S., Uga, H., & Iyatomi, H. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006567 · Cited by 24
Single- and multi-label classification of construction objects using deep transfer learning methods
Nath, N. D., Chaspari, T., & Behzadan, A. H. (2019) · Journal of Information Technology in Construction · doi:10.36680/j.itcon.2019.028 · Cited by 22
Momental directional patterns for dynamic texture recognition
Nguyễn, T. P., Nguyen, T. T., Bouchara, F., & Nguyen, X. S. (2019) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.102882 · Cited by 22
Deep Audio Priors Emerge From Harmonic Convolutional Networks
Zhang, Z., Wang, Y., Gan, C., Wu, J., Tenenbaum, J. B., Torralba, A. B., & Freeman, W. T. (2020) · International Conference on Learning Representations · Cited by 27
AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network
Rahnemoonfar, M., Johnson, J., & Paden, J. (2019) · Sensors · doi:10.3390/s19245479 · Cited by 23
Porn Streamer Recognition in Live Video Streaming via Attention-Gated Multimodal Deep Features
Wang, L., Zhang, J., Tian, Q., Li, C., & Li, Z. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2958871 · Cited by 21
Assets Predictive Maintenance Using Convolutional Neural Networks
Silva, W., & Capretz, M. A. M. (2019) · ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) · doi:10.1109/snpd.2019.8935752 · Cited by 26
Predicting Human Mobility via Attentive Convolutional Network
Miao, C., Luo, Z., Zeng, F., et al. (2020) · doi:10.1145/3336191.3371846 · Cited by 21
Intelligent Detection for Tunnel Shotcrete Spray Using Deep Learning and LiDAR
Chun-lei, L., Hao, S., Chun-Lai, L., & Li, J. (2019) · IEEE Access · doi:10.1109/access.2019.2962496 · Cited by 20
Zhang, W., Zhu, D., Zhang, N., et al. (2020) · IEEE Access · doi:10.1109/access.2019.2963504 · Cited by 21
Identifying Model Weakness with Adversarial Examiner
Shu, M., Liu, C., Qiu, W., & Yuille, A. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6876 · Cited by 22
Interference-aware parallelization for deep learning workload in GPU cluster
Geng, X., Zhang, H., Zhao, Z., & Ma, H. (2020) · Cluster Computing · doi:10.1007/s10586-019-03037-6 · Cited by 23
Lightweight residual densely connected convolutional neural network
Fooladgar, F., & Kasaei, S. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09223-8 · Cited by 21
Deep Learning Based Data Augmentation and Classification for Limited Medical Data Learning
Chen, H., & Cao, P. (2019) · IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS) · doi:10.1109/icpics47731.2019.8942411 · Cited by 21
Medical Image Analysis Using AM-FM Models and Methods
Constantinou, K. P., Constantinou, I. P., Pattichis, C. S., & Pattichis, M. S. (2020) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.2967273 · Cited by 22
Combining CNN streams of dynamic image and depth data for action recognition
Singh, R. K., Khurana, R., Kushwaha, A. K. S., et al. (2020) · Multimedia Systems · doi:10.1007/s00530-019-00645-5 · Cited by 21
Zhang, Y., Po, L.-M., Liu, M., Rehman, Y. A. U., Ou, W., & Zhao, Y. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113203 · Cited by 21
Song, G., Tao, Z., Huang, X., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2964836 · Cited by 21
Bi-Modal Learning With Channel-Wise Attention for Multi-Label Image Classification
Li, P., Chen, P., Xie, Y., & Zhang, D. (2020) · IEEE Access · doi:10.1109/access.2020.2964599 · Cited by 21
A Camera-based Smart Parking System Employing Low-complexity Deep Learning for Outdoor Environments
Polprasert, C., Sruayiam, C., Pisawongprakan, P., & Teravetchakarn, S. (2019) · International Conference on ICT and Knowledge Engineering · doi:10.1109/ictke47035.2019.8966901 · Cited by 24
Meitei Mayek handwritten dataset: compilation, segmentation, and character recognition
Inunganbi, S. C., Choudhary, P., & Manglem, K. (2020) · The Visual Computer · doi:10.1007/s00371-020-01799-4 · Cited by 22
Application of Convolutional Neural Networks for Fall Detection Using Multiple Cameras
Espinosa, R., Ponce-Espinosa, H., Gutiérrez, S., Martínez-Villaseñor, L., Brieva, J., & Moya-Albor, E. (2020) · Studies in systems, decision and control · doi:10.1007/978-3-030-38748-8_5 · Cited by 25
Quasi Fourier-Mellin Transform for Affine Invariant Features
Yang, J., Lu, Z., Tang, Y. Y., Yuan, Z., & Chen, Y. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2967578 · Cited by 21
Holistic design for deep learning-based discovery of tabular structures in datasheet images
Kara, E., Traquair, M., Şimşek, M., Kantarcı, B., & Khan, S. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.103551 · Cited by 21
Feature data processing: Making medical data fit deep neural networks
Xu, Y., Liu, Z., Li, Y., Hou, H., Cao, Y., Zhao, Y., Guo, W., & Cui, L. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.02.034 · Cited by 21
Intra-pulse modulation radar signal recognition based on Squeeze-and-Excitation networks
Wei, S., Qu, Q., Su, H., et al. (2020) · Signal Image and Video Processing · doi:10.1007/s11760-020-01652-0 · Cited by 21
Deep Learning Based Automated Attendance System
Athanesious, J. J., Vanitha, Adithya, S., Bhardwaj, C. A., Lamba, J. S., & Vaidehi, A. V. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2020.01.045 · Cited by 28
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
