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 44,001–44,100 of 108,439
Derin Öğrenme ve Destek Vektör Makineleri İle Görüntüden Cinsiyet Tahmini
Metlek, S., & Kayaalp, K. (2020) · Düzce Üniversitesi Bilim ve Teknoloji Dergisi · doi:10.29130/dubited.707316 · Cited by 18
Quantum-Inspired Evolutionary Algorithm for Convolutional Neural Networks Architecture Search
Ye, W., Liu, R., Li, Y., & Jiao, L. (2020) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec48606.2020.9185727 · Cited by 18
Song, W., Liu, L., Liu, M., Wang, W., Wang, X., & Song, Y. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-7981-3_22 · Cited by 16
An Efficient Accelerator Design Methodology For Deformable Convolutional Networks
Ahn, S., Chang, J.-W., & Kang, S. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9191028 · Cited by 15
Intra-Clip Aggregation For Video Person Re-Identification
Isobe, T., Han, J., Zhuz, F., et al. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9190839 · Cited by 15
Hierarchical Group Sparse Regularization for Deep Convolutional Neural Networks
Mitsuno, K., Miyao, J., & Kurita, T. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207531 · Cited by 15
Transformer-Based Multi-aspect Modeling for Multi-aspect Multi-sentiment Analysis
Wu, Z., Ying, C., Dai, X., Huang, S., & Chen, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60457-8_45 · Cited by 19
Li, Q., Zhu, W., Wu, C., Pan, X., Yang, F., Zhou, Y., & Zhang, Y. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413923 · Cited by 16
Comparing the performance of ResNets on COVID-19 diagnosis using CT scans
Cai, X., Wang, Y., Sun, X., et al. (2020) · International Conference on Computer, Information and Telecommunication Systems · doi:10.1109/cits49457.2020.9232574 · Cited by 15
Yue, J., Liu, Y., Liu, R., Sun, W., Yuan, Z., Tu, Y.-N., Chen, Y., Ren, A., Wang, Y., Chang, M., et al. (2020) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2020.3030264 · Cited by 16
Discrimination of Pepper Seed Varieties by Multispectral Imaging Combined with Machine Learning
Li, X., Fan, X., Zhao, L., et al. (2020) · Applied Engineering in Agriculture · doi:10.13031/aea.13794 · Cited by 15
An Improved Deep Belief Network Prediction Model Based on Knowledge Transfer
Zhang, Y., & Liu, F. (2020) · Future Internet · doi:10.3390/fi12110188 · Cited by 14
Rethinking the Defocus Blur Detection Problem and a Real-Time Deep DBD Model
Zhang, N., & Yan, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58607-2_36 · Cited by 15
Discriminative metric learning for face verification using enhanced Siamese neural network
Lü, T., Zhou, Q., Fang, W., et al. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09784-8 · Cited by 16
Large Batch Optimization for Object Detection: Training COCO in 12 minutes
Wang, T., Zhu, Y., Zhao, C., Zeng, W., Wang, Y., Wang, J., & Tang, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58589-1_29 · Cited by 14
Efficient Residue Number System Based Winograd Convolution
Liu, Z.-G., & Mattina, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58529-7_4 · Cited by 15
Fast signal quality monitoring for coherent communications enabled by CNN-based EVM estimation
Fan, Y., Udaļcovs, A., Pang, X., Natalino, C., Furdek, M., Popov, S., & Ozoliņš, O. (2020) · Journal of Optical Communications and Networking · doi:10.1364/jocn.409704 · Cited by 16
Fast Class-wise Updating for Online Hashing
Lin, M., Ji, R., Sun, X., Zhang, B., Huang, F., Tian, Y., & Tao, D. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3042193 · Cited by 27
Packet Vision: a convolutional neural network approach for network traffic classification
Moreira, R., Moreira, L. F. R., Rosa, P. F., Aguiar, R. L., & Silva, F. D. O. (2020) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi51738.2020.00042 · Cited by 18
Lei, X., Wang, H., Wang, C., et al. (2020) · Sensors · doi:10.3390/s20236969 · Cited by 15
Gudigar, A., Samanth, J., Raghavendra, U., Dharmik, C., Vasudeva, A., Ramachandran, P., Tan, R., Ciaccio, E. J., Molinari, F., & Acharya, U. R. (2020) · IEEE Access · doi:10.1109/access.2020.3042594 · Cited by 16
Jo, S., Park, C., Ryu, D.-W., et al. (2021) · Energies · doi:10.3390/en14020413 · Cited by 15
Deep Learning Assisted Zonal Adaptive Aberration Correction
Zhang, B., Zhu, J., Si, K., et al. (2021) · Frontiers in Physics · doi:10.3389/fphy.2020.621966 · Cited by 15
Lightweight Action Recognition in Compressed Videos
Huo, Y., Xu, X., Lu, Y., Niu, Y., Ding, M., Lu, Z., Xiang, T., & Wen, J.-R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66096-3_24 · Cited by 18
Vision-based Vehicle Detection and Distance Estimation
Qiao, D., & Zulkernine, F. H. (2020) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci47803.2020.9308364 · Cited by 15
Liu, H., Li, Q., & Wang, I.-C. (2021) · Mathematical Problems in Engineering · doi:10.1155/2021/6661083 · Cited by 15
Mask Guided Attention For Fine-Grained Patchy Image Classification
Wang, J., Yu, X., & Gao, Y. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506424 · Cited by 20
Lu, C., Jiang, H., You, C., et al. (2021) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2021.108471 · Cited by 15
Deep feature learning for gender classification with covered/camouflaged faces
Alghaili, M., Li, Z., & Ali, H. A. R. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2020.0199 · Cited by 15
Visual Interestingness Prediction: A Benchmark Framework and Literature Review
Constantin, M. G., Stefan, L., Ionescu, B. E., Duong, N. Q. K., Demarty, C.-H., & Sjöberg, M. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01443-1 · Cited by 17
Wang, Z., Xiao, Y., Weng, F., Li, X., Zhu, D., Lu, F., Liu, X., Hou, M., & Meng, Y. (2021) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-021-00432-7 · Cited by 15
Dual-channel convolutional neural network for power edge image recognition
Zhou, F., Ma, Y., Wang, B., et al. (2021) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-021-00235-9 · Cited by 15
Wang, F., Li, C., Zeng, Z., et al. (2021) · Applied Intelligence · doi:10.1007/s10489-020-02170-9 · Cited by 15
Sun, Z., Spyridis, Y., Λάγκας, Θ., Sesis, A., Efstathopoulos, G., & Sarigiannidis, P. G. (2021) · Sensors · doi:10.3390/s21051650 · Cited by 16
Contactless Fall Detection for the Elderly
Nahian, M. J. A., Raju, M. H., Tasnim, Z., Mahmud, M., Ahad, M., & Kaiser, M. (2021) · Intelligent systems reference library · doi:10.1007/978-3-030-68590-4_8 · Cited by 13
Yang, Z., Yang, X., Li, M., & Li, W. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106122 · Cited by 16
Separation of Aleatoric and Epistemic Uncertainty in Deterministic Deep Neural Networks
Huseljic, D., Sick, B., Herde, M., & Kottke, D. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412616 · Cited by 17
A Lightweight Dense Connected Approach with Attention on Single Image Super-Resolution
Zha, L., Yang, Y., Lai, Z., Zhang, Z., & Wen, J. (2021) · Electronics · doi:10.3390/electronics10111234 · Cited by 16
Generalization aspect of accurate machine learning models for CSI-based localization
Sobehy, A., Renault, É., & Mühlethaler, P. (2021) · Annals of Telecommunications · doi:10.1007/s12243-021-00853-z · Cited by 15
Efficient Folded Attention for Medical Image Reconstruction and Segmentation
Zhang, H., Zhang, J., Wang, R., Zhang, Q., Spincemaille, P., Nguyen, T. D., & Wang, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17298 · Cited by 16
Fast Vehicle Turning-Movement Counting using Localization-based Tracking
Gloudemans, D., & Work, D. B. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00469 · Cited by 18
Bipolar Morphological Neural Networks: Gate-Efficient Architecture for Computer Vision
Limonova, E. E., Alfonso, D. M., Nikolaev, D., & Arlazarov, V. V. (2021) · IEEE Access · doi:10.1109/access.2021.3094484 · Cited by 15
Adaptive and Collaborative Edge Inference in Task Stream with Latency Constraint
Song, J., Liu, Z., Wang, X., Qiu, C., & Chen, X. (2021) · International Conference on Communications · doi:10.1109/icc42927.2021.9500892 · Cited by 15
Zitzlsberger, G., Podhorányi, M., Svatoň, V., Lazecký, M., & Martinovič, J. (2021) · Remote Sensing · doi:10.3390/rs13153000 · Cited by 18
Leveraging Metadata in Representation Learning With Georeferenced Seafloor Imagery
Yamada, T., Massot‐Campos, M., Prügel‐Bennett, A., Williams, S. B., Pizarro, O. R., & Thornton, B. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3101881 · Cited by 15
Enabling Retrain-free Deep Neural Network Pruning Using Surrogate Lagrangian Relaxation
Gurevin, D., Bragin, M. A., Ding, C., Zhou, S., Pepin, L., Li, B., & Miao, F. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/344 · Cited by 20
Liu, B., Wang, L., Chen, X., et al. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9562018 · Cited by 15
Dynamic Domain Adaptation for Efficient Inference
Li, S., Zhang, J., Ma, W., Liu, C. H., & Li, W. (2021) · 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/cvpr46437.2021.00774 · Cited by 15
Neural networks can learn to utilize correlated auxiliary noise
Ahmadzadegan, A., Simidzija, P., Li, M., & Kempf, A. (2021) · Scientific Reports · doi:10.1038/s41598-021-00502-4 · Cited by 15
INVESTIGATION OF POINTNET FOR SEMANTIC SEGMENTATION OF LARGE-SCALE OUTDOOR POINT CLOUDS
Nurunnabi, A., Teferle, F. N., Li, J., et al. (2021) · 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-xlvi-4-w5-2021-397-2021 · Cited by 15
Tomesek, J., Čadík, M., & Brejcha, J. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00225 · Cited by 17
Jas, M., & Parikh, D. (2015) · 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.2015.7298889 · Cited by 14
Neural Networks for Sequential Data: a Pre‐training Approach based on Hidden Markov Models
Pasa, L., Testolin, A., & Sperduti, A. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.11.081 · Cited by 16
Learning two-pathway convolutional neural networks for categorizing scene images
Bai, S., Li, Z., & Hou, J. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3900-6 · Cited by 15
Deep learning feature representation for electrocardiogram identification
Lei, X., Zhang, Y., & Lu, Z. (2016) · IEEE International Conference on Digital Signal Processing (DSP) · doi:10.1109/icdsp.2016.7868505 · Cited by 15
Collision Risk Rating of Traffic Scene from Dashboard Cameras
Wang, Y., & Kato, J. (2017) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2017.8227417 · Cited by 15
Content-Based Image Retrieval using Deep Learning
Singh, A. V. (2015) · RIT Scholar Works (Rochester Institute of Technology) · Cited by 15
Deep Kernel LearningPreprint
Wilson, A. G., Hu, Z., Salakhutdinov, R., & Xing, E. P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.02222 · Cited by 319
Higher Order Potentials in End-to-End Trainable Conditional Random FieldsPreprint
Arnab, A., Jayasumana, S., Zheng, S., & Torr, P. H. S. (2015) · arXiv (Cornell University) · Cited by 14
Model Accuracy and Runtime Tradeoff in Distributed Deep LearningPreprint
Gupta, S., Zhang, W., & Milthorpe, J. (2015) · arXiv (Cornell University) · Cited by 16
Telugu OCR Framework using Deep LearningPreprint
Achanta, R., & Hastie, T. J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.05962 · Cited by 30
Zero-Shot Learning and Clustering for Semantic Utterance ClassificationPreprint
Dauphin, Y., Tür, G., Hakkani-Tür, D. Z., et al. (2013) · arXiv (Cornell University) · Cited by 15
Real-Time Video Highlights for Yahoo EsportsPreprint
Song, Y. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.08780 · Cited by 18
Neural dynamics of real-world object vision that guide behaviourPreprint
Cichy, R. M., Kriegeskorte, N., Jozwik, K. M., van den Bosch, J. J. F., & Charest, I. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/147298 · Cited by 14
Southeast Europe Journal of Soft Computing
(2017) · Southeast Europe Journal of Soft Computing · doi:10.21533/scjournal · Cited by 15
Deep Learning for Lip Reading using Audio-Visual Information for Urdu LanguagePreprint
Faisal, M., & Manzoor, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.05521 · Cited by 15
Contextual Multi-Scale Region Convolutional 3D Network for Activity DetectionPreprint
Bai, Y.-C., Xu, H., Saenko, K., & Ghanem, B. (2018) · arXiv.org · doi:10.48550/arxiv.1801.09184 · Cited by 14
Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form SolutionPreprint
Chu, L., Xia, H., Hu, J., Wang, L., & Pei, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.06259 · Cited by 15
Investigating the Impact of CNN Depth on Neonatal Seizure Detection PerformancePreprint
O'Shea, A., Lightbody, G., Boylan, G. B., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.03044 · Cited by 15
Hérent, P., Jégou, S., Wainrib, G., & Clozel, T. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/413302 · Cited by 16
Simple Physical Adversarial Examples against End-to-End Autonomous Driving ModelsPreprint
Boloor, A., He, X., Gill, C. D., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.05157 · Cited by 15
A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial ScenesPreprint
Mou, L., Hua, Y., & Zhu, X. X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.05730 · Cited by 15
Exploring Deep Physiological Models for Nociceptive Pain RecognitionPreprint
Thiam, P., Bellmann, P., Kestler, H. A., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/622431 · Cited by 15
Multinomial Distribution Learning for Effective Neural Architecture SearchPreprint
Zheng, X., Ji, R., Tang, L., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.07529 · Cited by 15
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust AccuraciesPreprint
Wang, B., Yuan, B., Shi, Z., & Osher, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.10745 · Cited by 44
Learning a Deep Embedding Model for Zero-Shot LearningPreprint
Zhang, L., Xiang, T., & Gong, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.05088 · Cited by 15
Pagès, G., & Grudinin, S. (2018) · arXiv (Cornell University) · Cited by 15
A Probabilistic Framework for Nonlinearities in Stochastic Neural NetworksPreprint
Su, Q., Liao, X., & Carin, L. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.06123 · Cited by 15
MoBiNet: A Mobile Binary Network for Image ClassificationPreprint
Phan, H., Huynh, D. N., He, Y., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.12629 · Cited by 15
Bakkouri, I., & Afde, K. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08859-w · Cited by 15
Recover and Identify: A Generative Dual Model for Cross-Resolution Person Re-IdentificationPreprint
Li, Y.-J., Chen, Y., Lin, Y., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.06052 · Cited by 15
Recognition Of Surface Defects On Steel Sheet Using Transfer LearningPreprint
Fu, J., Zhu, X., & Li, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.03258 · Cited by 18
Zeng, Y., Wu, J., & Xiao, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.06671 · Cited by 15
Pose-Guided Multi-Granularity Attention Network for Text-Based Person SearchPreprint
Jing, Y., Si, C., Wang, J., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.08440 · Cited by 15
Prototype Rectification for Few-Shot LearningPreprint
Liu, J., Song, L., & Qin, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.10713 · Cited by 15
Does AlphaGo actually play Go? Concerning the State Space of Artificial IntelligencePreprint
Lyre, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.10005 · Cited by 16
Dense RepPoints: Representing Visual Objects with Dense Point SetsPreprint
Yang, Z., Xu, Y., Xue, H., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.11473 · Cited by 15
Cross-dataset Training for Class Increasing Object DetectionPreprint
Yao, Y., Wang, Y., Guo, Y., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.04621 · Cited by 15
Sarıkaya, D., & Jannin, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.03728 · Cited by 19
Algorithms in Future Capital MarketsPreprint
Koshiyama, A., Firoozye, N. B., & Treleaven, P. C. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3527511 · Cited by 15
Learning Adaptive Loss for Robust Learning with Noisy LabelsPreprint
Shu, J., Zhao, Q., Chen, K., Xu, Z., & Meng, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.06482 · Cited by 15
Set2Graph: Learning Graphs From SetsPreprint
Serviansky, H., Segol, N., Shlomi, J., Cranmer, K. S., Gross, E., Maron, H., & Lipman, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.08772 · Cited by 19
Efficient Algorithms for Device Placement of DNN Graph OperatorsPreprint
Tarnawski, J., Phanishayee, A., Devanur, N. R., Mahajan, D., & Paravecino, F. N. (2020) · Neural Information Processing Systems · doi:10.48550/arxiv.2006.16423 · Cited by 87
Regularization Matters: A Nonparametric Perspective on Overparametrized Neural NetworkPreprint
Hu, T., Wang, W., Lin, C., & Cheng, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.02486 · Cited by 21
Class-wise Dynamic Graph Convolution for Semantic SegmentationPreprint
Hu, H., Ji, D., Gan, W., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.09690 · Cited by 15
Funnel Activation for Visual RecognitionPreprint
Ma, N., Zhang, X., & Sun, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.11824 · Cited by 15
Wireless for Machine LearningPreprint
Hellström, H., da Silva, J. M. D., Fodor, V., & Fischione, C. (2020) · arXiv.org · doi:10.48550/arxiv.2008.13492 · Cited by 19
Wong, A. K. C., Wang, X. Y., & Hryniowski, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.05835 · Cited by 22
Dong, J., Chen, S., Li, Y., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.14665 · Cited by 15
A Unified Approach to Interpreting and Boosting Adversarial TransferabilityPreprint
Wang, X., Ren, J., Lin, S., Zhu, X., Wang, Y., & Zhang, Q. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.04055 · Cited by 66
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
