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 34,501–34,600 of 108,435
Disease classification in Solanum melongena using deep learning
Aravind, K. R., Raja, P., Ashiwin, R., et al. (2019) · Spanish Journal of Agricultural Research · doi:10.5424/sjar/2019173-14762 · Cited by 23
Thalhammer, S., Patten, T., & Vincze, M. (2019) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2019.00021 · Cited by 23
A Deep Learning Based Resource Allocation Scheme in Vehicular Communication Systems
Chen, M., Chen, J., Chen, X., Zhang, S., & Xu, S. (2019) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc.2019.8886105 · Cited by 25
Semi-Supervised Complex-Valued GAN for Polarimetric SAR Image Classification
Sun, Q., Li, X., Li, L., et al. (2019) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2019.8898217 · Cited by 23
Integrating Multi-Omic Data With Deep Subspace Fusion Clustering for Cancer Subtype Prediction
Yang, B., Zhang, Y., Pang, S., Shang, X., Zhao, X., & Han, M. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2951413 · Cited by 24
Security Analysis of Radar Systems
Cohen, S., Gluck, T., Elovici, Y., & Shabtai, A. (2019) · doi:10.1145/3338499.3357363 · Cited by 24
Spatiotemporal Fusion of Satellite Images via Very Deep Convolutional Networks
Zheng, Y., Song, H., Sun, L., Wu, Z., & Jeon, B. (2019) · Remote Sensing · doi:10.3390/rs11222701 · Cited by 25
An Evaluation of Parametric Activation Functions for Deep Learning
Godfrey, L. B. (2019) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2019.8913972 · Cited by 25
A review of Visual-Based Localization
Xin, X., Jiang, J., & Zou, Y. (2019) · International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI) · doi:10.1145/3366194.3366211 · Cited by 27
Deep Face Image Retrieval for Cancelable Biometric Authentication
Jang, Y. K., & Cho, N. I. (2019) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2019.8909878 · Cited by 23
Embarrassingly Simple Binary Representation Learning
Shen, Y., Qin, J., Chen, J., Liu, L., Zhu, F., & Shen, Z. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00350 · Cited by 23
Fall Detection on a single Doppler Radar Sensor by using Convolutional Neural Networks
Yoshino, H., Moshnyaga, V. G., & Hashimoto, K. (2019) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2019.8914303 · Cited by 23
The Evaluation of Word Embedding Models and Deep Learning Algorithms for Turkish Text Classification
Kilimci, Z. H., & Akyokuş, S. (2019) · International Conference on Computer Science and Engineering (UBMK) · doi:10.1109/ubmk.2019.8907027 · Cited by 28
Adversarial Risk via Optimal Transport and Optimal Couplings
Pydi, M. S., & Jog, V. (2021) · IEEE Transactions on Information Theory · doi:10.1109/tit.2021.3100107 · Cited by 32
A Deep Learning Framework for Transforming Image Reconstruction Into Pixel Classification
Pawar, K., Chen, Z., Shah, N. J., & Egan, G. F. (2019) · IEEE Access · doi:10.1109/access.2019.2959037 · Cited by 23
Multi-Factor Operating Condition Recognition Using 1D Convolutional Long Short-Term Network
Jiang, Z., Lai, Y., Zhang, J., Zhao, H., & Mao, Z. (2019) · Sensors · doi:10.3390/s19245488 · Cited by 22
Data Augmentation Applied to Machine Learning-Based Monitoring of a Pulp and Paper Process
Parente, A. P., de Souza, M. B., Jr., Valdman, A., & Folly, R. O. M. (2019) · Processes · doi:10.3390/pr7120958 · Cited by 23
Learning efficient haptic shape exploration with a rigid tactile sensor array
Fleer, S., Moringen, A., Klatzky, R. L., & Ritter, H. J. (2020) · PLoS ONE · doi:10.1371/journal.pone.0226880 · Cited by 25
Weakly Supervised Representation Learning for Audio-Visual Scene Analysis
Parekh, S., Essid, S., Ozerov, A., Duong, N. Q. K., Pérez, P., & Gaël Richard, A. (2019) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2019.2957889 · Cited by 39
Xu, J., Lv, H., Zhuang, Z., Lu, Z., Zou, D., & Qin, W. (2019) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2019.11.418 · Cited by 26
Canopy Segmentation Using ResNet for Mechanical Harvesting of Apples
Zhang, X., Fu, L., Karkee, M., et al. (2019) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2019.12.550 · Cited by 23
Global Optimal Structured Embedding Learning for Remote Sensing Image Retrieval
Liu, P., Gou, G., Xue, S., Tao, D., & Zhou, Q. (2020) · Sensors · doi:10.3390/s20010291 · Cited by 23
On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks
Li, X., Li, X., Pan, D., & Zhu, D. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5907 · Cited by 25
Khan, Z., & Yang, J. (2020) · Image and Vision Computing · doi:10.1016/j.imavis.2020.103871 · Cited by 23
Iriya, R., Jing, W., Syal, K., et al. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2967058 · Cited by 23
Two-Stream Video Classification with Cross-Modality Attention
Chi, L., Tian, G., Mu, Y., & Tian, Q. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00552 · Cited by 28
Web-Based Cataract Detection System Using Deep Convolutional Neural Network
Yusuf, M., Theophilous, S., Adejoke, J., et al. (2019) · doi:10.1109/nigeriacomputconf45974.2019.8949636 · Cited by 23
Tree Cover Estimation in Global Drylands from Space Using Deep Learning
Guirado, E., Alcaraz‐Segura, D., Cabello, J., Puertas-Ruíz, S., Herrera, F., & Tabik, S. (2020) · Remote Sensing · doi:10.3390/rs12030343 · Cited by 26
Three-way decisions based blocking reduction models in hierarchical classification
Shen, W., Wei, Z., Li, Q., et al. (2020) · Information Sciences · doi:10.1016/j.ins.2020.02.020 · Cited by 23
Dong, Z., Men, J., Yang, Z., et al. (2020) · Biomedical Optics Express · doi:10.1364/boe.385968 · Cited by 23
Smart Tracking of Internal Layers of Ice in Radar Data via Multi-Scale Learning
Yari, M., Rahnemoonfar, M., Paden, J., Ibikunle, O., Koenig, L. S., & Montgomery, L. N. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006083 · Cited by 23
Deep Multiphase Level Set for Scene Parsing
Zhang, P., Liu, W., Lei, Y., Wang, H., & Lu, H. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2957915 · Cited by 23
Lane detection using lane boundary marker network with road geometry constraints
Khan, H. U., Ali, A. R., Hassan, A., et al. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093296 · Cited by 23
Xie, Z., Ling, T., Yang, Y., et al. (2020) · Journal of Medical Systems · doi:10.1007/s10916-020-01561-2 · Cited by 23
Zhang, H., Saravanan, K. M., Lin, J., Liao, L., Ng, J. T.-Y., Zhou, J., & Wei, Y. (2020) · PeerJ · doi:10.7717/peerj.8864 · Cited by 23
Singh, J. M., Ramachandra, R., Raja, K. B., et al. (2019) · International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) · doi:10.1109/sitis.2019.00028 · Cited by 23
Prediction of miRNA targets by learning from interaction sequences
Zheng, X., Chen, L., Li, X., Zhang, Y., Xu, S., & Huang, X. (2020) · PLoS ONE · doi:10.1371/journal.pone.0232578 · Cited by 24
Attribute-Cooperated Convolutional Neural Network for Remote Sensing Image Classification
Zhang, Y., Zheng, X., Yuan, Y., et al. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.2987338 · Cited by 23
Lin, C., Lin, C., & Jeng, S.-Y. (2020) · Applied Sciences · doi:10.3390/app10093166 · Cited by 22
Dataset for Eye Tracking on a Virtual Reality Platform
Garbin, S. J., Komogortsev, O. V., Cavin, R., Hughes, G., Shen, Y., Schuetz, I., & Talathi, S. S. (2020) · ACM Symposium on Eye Tracking Research and Applications (ETRA) · doi:10.1145/3379155.3391317 · Cited by 27
A High-Accuracy Implementation for Softmax Layer in Deep Neural Networks
Alabassy, B., Safar, M., & El-Kharashi, M. W. (2020) · International Conference on Design & Technology of Integrated Systems in Nanoscale Era (DTIS) · doi:10.1109/dtis48698.2020.9081313 · Cited by 24
Cheng, T., Masuda, Y., Chen, J., Yu, J., & Hashimoto, M. (2020) · Integration · doi:10.1016/j.vlsi.2020.05.002 · Cited by 23
Khatun, A., Denman, S., Sridharan, S., & Fookes, C. (2020) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2020.102989 · Cited by 25
Wang, F., Zhou, X., Wang, J., Zhang, X., He, Z., & Song, B. (2020) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2020.2997799 · Cited by 24
Multi-Modal Fingerprint Presentation Attack Detection: Evaluation on a New Dataset
Spinoulas, L., Mirzaalian, H., Hussein, M. E., & AbdAlmageed, W. (2021) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2021.3072325 · Cited by 27
Feature Statistics Guided Efficient Filter Pruning
Li, H., Ma, C., Xu, W. H., & Liu, X. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/363 · Cited by 24
Introduction to deep learning: minimum essence required to launch a research
Wataya, T., Nakanishi, K., Suzuki, Y., Kido, S., & Tomiyama, N. (2020) · Japanese Journal of Radiology · doi:10.1007/s11604-020-00998-2 · Cited by 24
Adaptive Block Compressive Sensing: Toward a Real-Time and Low-Complexity Implementation
Zammit, J. A., & Wassell, I. J. (2020) · IEEE Access · doi:10.1109/access.2020.3006861 · Cited by 23
Remote-Sensing Image Scene Classification With Deep Neural Networks in JPEG 2000 Compressed Domain
Byju, A. P., Sümbül, G., Demir, B., & Bruzzone, L. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3007523 · Cited by 24
Online Hashing With Bit Selection for Image Retrieval
Weng, Z., & Zhu, Y. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3004962 · Cited by 23
Res-VGG: A Novel Model for Plant Disease Detection by Fusing VGG16 and ResNet Models
Kumar, A., Razi, R., Singh, A. K., & Das, H. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-6318-8_32 · Cited by 24
Deep Learning for Scene Recognition from Visual Data: A Survey
Matei, A., Glavan, A. I., & Talavera, E. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61705-9_64 · Cited by 29
Double Graph Regularized Double Dictionary Learning for Image Classification
Rong, Y., Xiong, S., & Gao, Y. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3004246 · Cited by 23
Chest X-Ray Images Based Automated Detection of Pneumonia Using Transfer Learning and CNN
Thakur, S., Goplani, Y., Arora, S., Upadhyay, R., & Sharma, G. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-4992-2_31 · Cited by 23
Prediction of Heavy Rain Damage Using Deep Learning
Lee, K., Choi, C., Shin, D. H., & Kim, H. S. (2020) · Water · doi:10.3390/w12071942 · Cited by 23
Defect Detection Method for Electric Multiple Units Key Components Based on Deep Learning
Zhao, B., Dai, M., Li, P., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3009654 · Cited by 23
An End to End Deep Neural Network for Iris Recognition
Hu, Q., Yin, S., Ni, H., et al. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.06.118 · Cited by 23
Convolutional Neural Network Based Breast Cancer Histopathology Image Classification
Yamlome, P., Akwaboah, A. D., Marz, A., et al. (2020) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc44109.2020.9176594 · Cited by 23
Understanding adversarial robustness via critical attacking route
Li, T., Liu, A., Liu, X., et al. (2020) · Information Sciences · doi:10.1016/j.ins.2020.08.043 · Cited by 23
Energy Efficient Fixed-point Inference System of Convolutional Neural Network
Lo, C. Y., & Sham, C. (2020) · IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) · doi:10.1109/mwscas48704.2020.9184436 · Cited by 24
Learning adaptive geometry for unsupervised domain adaptation
Yang, B., & Yuen, P. C. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107638 · Cited by 24
Context-aware network for RGB-D salient object detection
Liang, F., Duan, L., Ma, W., Qiao, Y., Miao, J., & Ye, Q. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107630 · Cited by 24
FFA-DMRI: A Network Based on Feature Fusion and Attention Mechanism for Brain MRI Denoising
Hong, D., Huang, C., Yang, C., Li, J., Qian, Y., & Cai, C. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.577937 · Cited by 23
Karabağ, C., Jones, M. L., Peddie, C. J., Weston, A., Collinson, L., & Reyes‐Aldasoro, C. C. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230605 · Cited by 23
XAI-Driven Explainable Multi-view Game Cheating Detection
Tao, J., Xiong, Y., Zhao, S.-W., Xu, Y., Lin, J., Wu, R.-Z., & Fan, C.-J. (2020) · 2020 IEEE Conference on Games (CoG) · doi:10.1109/cog47356.2020.9231843 · Cited by 25
BANet: A Balanced Atrous Net Improved From SSD for Autonomous Driving in Smart Transportation
Xu, X., Zhao, J., Li, Y., Gao, H., & Wang, X. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3034356 · Cited by 23
Shen, C., Tsai, M.-Y., Chen, L., Li, S., Nguyen, D. T. D., Wang, J., Jiang, S. B., & Jia, X. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/abc812 · Cited by 23
Visual Heart Rate Estimation from Facial Video Based on CNN
Huang, B., Chang, C.-M., Lin, C., Chen, W., Juang, C., & Wu, X. (2020) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea48937.2020.9248356 · Cited by 28
Learning to Stop: A Simple yet Effective Approach to Urban Vision-Language Navigation
Xiang, J., Wang, X., & Wang, W. Y. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.62 · Cited by 27
Scene Text Detection in Natural Images: A Review
Cao, D., Zhong, Y., Wang, L., He, Y., & Dang, J. (2020) · Symmetry · doi:10.3390/sym12121956 · Cited by 27
Tensor Dropout for Robust Learning
Kolbeinsson, A., Kossaifi, J., Panagakis, Y., Bulat, A., Anandkumar, A., Tzoulaki, I., & Matthews, P. M. (2021) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2021.3064182 · Cited by 23
Jegorowa, A., Kurek, J., Antoniuk, I., et al. (2020) · Wood Science and Technology · doi:10.1007/s00226-020-01245-7 · Cited by 23
Skovsen, S., Laursen, M. S., Kristensen, R. K., et al. (2020) · Sensors · doi:10.3390/s21010175 · Cited by 23
Assisting humans in privacy management: an agent-based approach
Kurtan, C., & Yolum, P. (2020) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-020-09488-1 · Cited by 25
Pyramid regional graph representation learning for content-based video retrieval
Zhao, G., Zhang, M., Li, Y., Liu, J., Zhang, B., & Wen, J.-R. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2020.102488 · Cited by 23
JellyNet: The convolutional neural network jellyfish bloom detector
Mcilwaine, B., & Casado, M. R. (2021) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2020.102279 · Cited by 24
Dynamic Workload Allocation for Edge Computing
Hung, Y.-W., Chen, Y., Lo, C., So, A. G., & Chang, S.-C. (2021) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2021.3049520 · Cited by 25
A Systematic Comparison of Depth Map Representations for Face Recognition
Pini, S., Borghi, G., Vezzani, R., Maltoni, D., & Cucchiara, R. (2021) · Sensors · doi:10.3390/s21030944 · Cited by 23
Aljemely, A. H., Xuan, J., Xu, L., Jawad, F. K. J., & Al-Azzawi, O. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02252-2 · Cited by 26
A method for classifying citrus surface defects based on machine vision
Zhang, W., Tan, A., Zhou, G., et al. (2021) · Journal of Food Measurement & Characterization · doi:10.1007/s11694-021-00868-z · Cited by 23
Wearable EEG-based Activity Recognition in PHM-related Service Environment via Deep Learning
Sarkar, S., Reddy, K., Dorgan, A., Fidopiastis, C., & Giering, M. J. (2020) · International Journal of Prognostics and Health Management · doi:10.36001/ijphm.2016.v7i4.2459 · Cited by 22
Al-Tahan, H., & Mohsenzadeh, Y. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008775 · Cited by 23
3D shallow deep neural network for fast and precise segmentation of left atrium
Kausar, A., Razzak, I., Shapiai, I., & Beheshti, A. (2021) · Multimedia Systems · doi:10.1007/s00530-021-00776-8 · Cited by 25
Learned Metric Index — Proposition of learned indexing for unstructured data
Antol, M., Oľha, J., Slanináková, T., et al. (2021) · Information Systems · doi:10.1016/j.is.2021.101774 · Cited by 23
Single-cell multi-omics sequencing: application trends, COVID-19, data analysis issues and prospects
Huo, L., Li, J. J., Chen, L., Yu, Z., Hutvágner, G., & Li, J. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab229 · Cited by 27
Gibbon, S., Attaheri, A., Choisdealbha, Á. N., Rocha, S., Brusini, P., Mead, N., Boutris, P., Olawole‐Scott, H., Ahmed, H., Flanagan, S. A., et al. (2021) · Brain and Language · doi:10.1016/j.bandl.2021.104968 · Cited by 20
A cascade model-aware generative adversarial example detection method
Han, K., Li, Y., & Xia, B. (2021) · Tsinghua Science & Technology · doi:10.26599/tst.2020.9010038 · Cited by 23
Fast Walsh-Hadamard Transform and Smooth-Thresholding Based Binary Layers in Deep Neural Networks
Pan, H., Badawi, D., & Çetin, A. E. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00523 · Cited by 25
Hierarchical Reasoning Network for Pedestrian Attribute Recognition
An, H., Hu, H., Guo, Y., Zhou, Q., & Li, B. H. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.2975417 · Cited by 23
Multivariate Time-series Anomaly Detection using SeqVAE-CNN Hybrid Model
Choi, T., Lee, D. K., Jung, Y., & Choi, H. (2022) · International Conference on Information Networking · doi:10.1109/icoin53446.2022.9687205 · Cited by 30
Multi target pigs tracking loss correction algorithm based on Faster R-CNN
Sun, L., Zou, Y., Li, Y., Cai, Z., Li, Y., Luo, B., Liu, Y., & Li, Y. (2018) · International journal of agricultural and biological engineering · doi:10.25165/j.ijabe.20181105.4232 · Cited by 26
Deep Learning Framework for Recognition of Cattle Using Muzzle Point Image Pattern
Kumar, S., Singh, S. K., Singh, R., et al. (2017) · Springer eBooks · doi:10.1007/978-981-10-7956-6_6 · Cited by 23
Large-Scale Image Mining with Flickr Groups
Ginsca, A., Popescu, A., Borgne, H. L., Ballas, N., Vo, P., & Kanellos, I. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-14445-0_28 · Cited by 23
A Novel method for visually impaired using object recognition
Kumar, R., & Meher, S. (2015) · International Conference on Communications and Signal Processing (ICCSP) · doi:10.1109/iccsp.2015.7322596 · Cited by 24
PPP-Codes for Large-Scale Similarity Searching
Novák, D., & Zezula, P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-662-49214-7_2 · Cited by 24
Development of the MIT ASR system for the 2016 Arabic Multi-genre Broadcast Challenge
Alhanai, T., Hsu, W.-N., & Glass, J. R. (2016) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2016.7846280 · Cited by 23
Yuen, K., Martin, S., & Trivedi, M. M. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900056 · Cited by 22
Tattoo detection and localization using region-based deep learning
Sun, Z. H., Baumes, J., Tunison, P., et al. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900103 · Cited by 23
Deep Regression for Face AlignmentPreprint
Shi, B., Bai, X., Liu, W., et al. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.5230 · Cited by 23
Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable SensorsPreprint
Zhao, Y., Yang, R., Chevalier, G., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.08989 · Cited by 23
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
