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 42,201–42,300 of 108,439
Joint Entity-Relation Extraction via Improved Graph Attention Networks
Lai, Q., Zhou, Z., & Liu, S. (2020) · Symmetry · doi:10.3390/sym12101746 · Cited by 16
Behavior-driven Student Performance Prediction with Tri-branch Convolutional Neural Network
Zong, J., Cui, C., Ma, Y., Yao, L., Chen, M., & Yin, Y. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3412110 · Cited by 17
A Light-weight Blockchain Architecture for V2V Knowledge Sharing at Vehicular Edges
Islam, S., Badsha, S., & Sengupta, S. (2020) · IEEE International Smart Cities Conference (ISC2) · doi:10.1109/isc251055.2020.9239055 · Cited by 16
Robust Deep Neural Network Model for Identification of Malaria Parasites in Cell Images
Huq, A., & Pervin, M. T. (2020) · IEEE Region 10 Symposium (TENSYMP) · doi:10.1109/tensymp50017.2020.9230832 · Cited by 17
Altis: Modernizing GPGPU Benchmarks
Hu, B., & Rossbach, C. J. (2020) · IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) · doi:10.1109/ispass48437.2020.00011 · Cited by 17
Deep learning surrogate models for spatial and visual connectivity
Tarabishy, S., Psarras, S., Kosicki, M., et al. (2020) · UCL Discovery (University College London) · Cited by 16
A little goes a long way: Improving toxic language classification despite data scarcity
Juuti, M., Gröndahl, T., Flanagan, A., & Asokan, N. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.269 · Cited by 23
SRARNet: A Unified Framework for Joint Superresolution and Aircraft Recognition
Tang, W., Deng, C., Han, Y., et al. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3037225 · Cited by 16
Influence of the receptive field size on accuracy and performance of a convolutional neural network
Gabbasov, R., & Александрович, П. Р. (2020) · International Conference on Information Technology and Nanotechnology (ITNT) · doi:10.1109/itnt49337.2020.9253219 · Cited by 16
Liu, H., Liu, Y., Wong, L. P., Lee, L., & Hao, T. (2020) · Complexity · doi:10.1155/2020/8858852 · Cited by 19
Improved Prototypical Network Model for Forest Species Classification in Complex Stand
Tian, X., Chen, L., Zhang, X., & Chen, E. (2020) · Remote Sensing · doi:10.3390/rs12223839 · Cited by 16
Multi-scale attention U-net for segmenting clinical target volume in graves’ ophthalmopathy
Hu, J., Song, Y., Zhang, L., Bai, S., & Zhang, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.11.028 · Cited by 16
Rundo, F., Banna, G. L., Prezzavento, L., Trenta, F., Sabrina Conoci, A., & Battiato, S. (2020) · Journal of Imaging · doi:10.3390/jimaging6120133 · Cited by 15
Feature Guided CNN for Baby’s Facial Expression Recognition
Lin, Q., He, R., & Jiang, P. (2020) · Complexity · doi:10.1155/2020/8855885 · Cited by 17
Are Deep Neural Architectures Losing Information? Invertibility is Indispensable
Liu, Y., Qin, Z., Anwar, S., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-63836-8_15 · Cited by 18
Saenz, H., Sun, H., Wu, L., Zhou, X., & Yu, H. (2020) · IET Intelligent Transport Systems · doi:10.1049/itr2.12012 · Cited by 15
Multimodal Deep Fusion Network for Visibility Assessment With a Small Training Dataset
Wang, H., Shen, K., Yu, P., Shi, Q., & Ko, H. (2020) · IEEE Access · doi:10.1109/access.2020.3031283 · Cited by 17
TransNet: Unseen Malware Variants Detection Using Deep Transfer Learning
Rong, C., Gou, G., Cui, M., Xiong, G., Li, Z., & Guo, L. (2020) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-63095-9_5 · Cited by 17
Neary, P. L., Watnik, A. T., Judd, K. P., Lindle, J. R., & Flann, N. S. (2020) · Applied Sciences · doi:10.3390/app10248782 · Cited by 19
Fortuna-Cervantes, J. M., Ramírez-Torres, M. T., Martínez-Carranza, J., et al. (2020) · Programming and Computer Software · doi:10.1134/s0361768820080113 · Cited by 16
Javanmardi, M., Song, Z., & Qi, X. (2020) · Applied Sciences · doi:10.3390/app11010309 · Cited by 17
Knowledge-based generative adversarial networks for scene understanding in Cultural Heritage
Garozzo, R., Santagati, C., Spampinato, C., et al. (2020) · Journal of Archaeological Science Reports · doi:10.1016/j.jasrep.2020.102736 · Cited by 16
AF-EMS Detector: Improve the Multi-Scale Detection Performance of the Anchor-Free Detector
Yan, J., Zhao, L., Diao, W., Wang, H., & Sun, X. (2021) · Remote Sensing · doi:10.3390/rs13020160 · Cited by 16
3DPoseLite: A Compact 3D Pose Estimation Using Node Embeddings
Dani, M., Narain, K., & Hebbalaguppe, R. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00192 · Cited by 14
Railroad Surface Defect Segmentation Using a Modified Fully Convolutional Network
Kim, H., Lee, S., & Han, S. (2020) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2020.12.008 · Cited by 16
Hierarchical Visual-Textual Knowledge Distillation for Life-Long Correlation Learning
Peng, Y., Qi, J., Ye, Z., & Zhuo, Y. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01392-1 · Cited by 16
Tahir, M., Hayat, M., & Chong, K. T. (2021) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2021.104233 · Cited by 16
Elghamrawy, S. M. (2020) · Studies in big data · doi:10.1007/978-3-030-55258-9_16 · Cited by 16
(2021) · doi:10.23915/distill.00024.005 · Cited by 32
Guo, S., Liu, F., Yuan, X., et al. (2021) · Tsinghua Science & Technology · doi:10.26599/tst.2020.9010011 · Cited by 16
Automatic plankton image classification—Can capsules and filters help cope with data set shift?
Plonus, R., Conradt, J., Harmer, A., Janßen, S., & Floeter, J. (2021) · Limnology and Oceanography Methods · doi:10.1002/lom3.10413 · Cited by 17
Patino, C. A., Mukherjee, P., Lemaitre, V., Pathak, N., & Espinosa, H. D. (2021) · SLAS TECHNOLOGY · doi:10.1177/2472630320982320 · Cited by 16
A Review of Machine Learning Methodologies for Dental Disease Detection
Chitnis, G., Bhanushali, V., Ranade, A., Khadase, T., Pelagade, V., & Chavan, J. (2020) · IEEE India Council International Subsections Conference (INDISCON) · doi:10.1109/indiscon50162.2020.00025 · Cited by 16
Abnormal crowd density estimation in aerial images based on the deep and handcrafted features fusion
Bouhlel, F., Mliki, H., & Hammami, M. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114656 · Cited by 16
Driver Drowsiness Detection Using Facial Parameters and RNNs with LSTM
Yarlagadda, V., Koolagudi, S. G., Manoj Kumar, M., V, et al. (2020) · IEEE India Council International Conference (INDICON) · doi:10.1109/indicon49873.2020.9342348 · Cited by 16
Kurek, J., Aleksiejuk-Gawron, J., Antoniuk, I., et al. (2019) · Machine Graphics and Vision · doi:10.22630/mgv.2019.28.1.1 · Cited by 16
Takeout Service Automation With Trained Robots in the Pandemic-Transformed Catering Business
Lin, T.-Y., Wu, K.-R., Chen, Y.-S., et al. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3052451 · Cited by 16
Automated Pixel-Wise Pavement Crack Detection by Classification-Segmentation Networks
Yu, B., Meng, X., & Yu, Q. (2021) · Journal of Transportation Engineering Part B Pavements · doi:10.1061/jpeodx.0000253 · Cited by 16
Structured Sparsity of Convolutional Neural Networks via Nonconvex Sparse Group Regularization
Bui, K., Park, F., Zhang, S., Qi, Y., & Xin, J. X. (2021) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2020.529564 · Cited by 17
Segmentation of the Airway Tree From Chest CT Using Tiny Atrous Convolutional Network
Cheng, G., Wu, X., Xiang, W., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3059680 · Cited by 16
E-ComSupResNet: Enhanced Face Super-Resolution Through Compact Network
Chudasama, V. M., Nighania, K., Upla, K. P., Raja, K. B., Ramachandra, R., & Busch, C. (2021) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2021.3059196 · Cited by 16
Rupture Detection During Needle Insertion Using Complex OCT Data and CNNs
Latus, S., Sprenger, J., Neidhardt, M., Schädler, J., Ron, A., Fitzek, A., Schlüter, M., Breitfeld, P., Heinemann, A., Püschel, K., et al. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3063069 · Cited by 17
Improved CNN-Based Hashing for Encrypted Image Retrieval
Pan, W., Wang, M., Qin, J., & Zhou, Z. (2021) · Security and Communication Networks · doi:10.1155/2021/5556634 · Cited by 16
Residual U-Network for Breast Tumor Segmentation from Magnetic Resonance Images
Anand, I., Negi, H., Kumar, D., Mittal, M., Kim, T.-H., & Roy, S. S. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.014229 · Cited by 16
Model Free Identification of Traffic Conditions Using Unmanned Aerial Vehicles and Deep Learning
Vlahogianni, E. I., Ser, J. D., Kepaptsoglou, K. L., & Laña, I. (2021) · Journal of Big Data Analytics in Transportation · doi:10.1007/s42421-021-00038-z · Cited by 17
Sun, Y., Dong, M., Yu, M., Xia, J., Zhang, X., Bai, Y., Lu, L., & Zhu, L. (2021) · International Journal of Optics · doi:10.1155/2021/6667495 · Cited by 16
Heart Failure Prediction by Feature Ranking Analysis in Machine Learning
Pushpavathi, T. P., Kumari, S., & Kubra, N. K. (2021) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict50816.2021.9358733 · Cited by 16
Video-Based Air Quality Measurement With Dual-Channel 3-D Convolutional Network
Wang, Z., Yue, S., & Song, C. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3068375 · Cited by 17
Deep Learning in Skin Lesion Analysis Towards Cancer Detection
Waweru, A. K., Ahmed, K. E. U., Miao, Y., & Kawan, P. (2020) · International Conference on Information Visualisation (IV) · doi:10.1109/iv51561.2020.00130 · Cited by 20
Large-scale gastric cancer screening and localization using multi-task deep neural network
Yu, H., Zhang, X., Song, L., Jiang, L., Huang, X., Chen, W., Zhang, C., Li, J., Yang, J., Hu, Z., et al. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.006 · Cited by 16
Artificial and beneficial – Exploiting artificial images for aerial vehicle detection
Weber, I., Bongartz, J., & Roscher, R. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.02.015 · Cited by 17
Sliding window based deep ensemble system for breast cancer classification
Alqudah, A., & Alqudah, A. M. (2021) · Journal of Medical Engineering & Technology · doi:10.1080/03091902.2021.1896814 · Cited by 15
AI as an Essential Element of a Green 6G
Chih‐Lin, I. (2021) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2021.3057247 · Cited by 16
Zhan, T., Ye, Q., Xiao, D., & Meng, G. (2021) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2021.106619 · Cited by 16
Game Theory Based Privacy Preserving Approach for Collaborative Deep Learning in IoT
Gupta, D., Bhatt, S., Bhatt, P., Gupta, M., & Tosun, A. Ş. (2021) · Signals and communication technology · doi:10.1007/978-981-16-6186-0_8 · Cited by 19
Brain Tumors Classification for MR images based on Attention Guided Deep Learning Model
Zhang, Y., Wang, S., Wu, H., et al. (2021) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc46164.2021.9630571 · Cited by 16
PMT-Net: Progressive Multi-Task Network for one-shot Person Re-Identification
Zhang, Y., Ma, B., Feng, Y., et al. (2021) · Information Sciences · doi:10.1016/j.ins.2021.03.048 · Cited by 16
Evolving Fully Automated Machine Learning via Life-Long Knowledge Anchors
Zheng, X., Zhang, Y., Hong, S., Li, H., Tang, L., Xiong, Y., Zhou, J., Wang, Y., Sun, X., Zhu, P., et al. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3069250 · Cited by 17
Syntactic- and morphology-based text augmentation framework for Arabic sentiment analysis
Duwairi, R., & Shaqra, F. A. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.469 · Cited by 16
Vimal, S., Robinson, H., Kaliappan, M., et al. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-021-03757-w · Cited by 16
RetractedGastric Tract Disease Recognition Using Optimized Deep Learning Features
Nayyar, Z., Khan, M. A., Alhussein, M. A., et al. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.015916 · Cited by 16
Uncertainties in Neural Networks : A System Identification Approach
Malmström, M. (2021) · Linköping studies in science and technology. Thesis · doi:10.3384/lic.diva-174720 · Cited by 16
Similarity learning to enable building searches in post‐event image data
Choi, J. B., Park, J. A., Dyke, S. J., et al. (2021) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12698 · Cited by 16
Birds Identification System using Deep Learning
Al-Showarah, S., & Sohyb, T. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0120434 · Cited by 16
Ball-Catching System Using Image Processing and an Omni-Directional Wheeled Mobile Robot
Kao, S.-T., & Ho, M. (2021) · Sensors · doi:10.3390/s21093208 · Cited by 16
Lunar features detection for energy discovery via deep learning
Chen, S., Li, Y., Zhang, T., Zhu, X., Sun, S., & Gao, X. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117085 · Cited by 16
Ear-based biometric authentication through the detection of prominent contours
Kohlakala, A., & Coetzer, J. (2021) · SAIEE Africa Research Journal · doi:10.23919/saiee.2021.9432897 · Cited by 15
Qiu, H., Ma, H., Zhang, Z., Gao, Y., Zheng, Y., Fu, A., Zhou, P., Abbott, D., & Al-Sarawi, S. F. (2022) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2022.3197499 · Cited by 16
Adetiba, E., Ajayi, O. T., Kala, J. R., et al. (2021) · Journal of Computer Science · doi:10.3844/jcssp.2021.349.363 · Cited by 16
Learning Disentangled Representations for Identity Preserving Surveillance Face Camouflage
Li, J., Han, L., Zhang, H., et al. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412636 · Cited by 16
Classification of strawberry diseases and pests by improved AlexNet deep learning networks
Dong, C., Zhang, Z., Yue, J., et al. (2021) · International Conference on Advanced Computational Intelligence (ICACI) · doi:10.1109/icaci52617.2021.9435893 · Cited by 16
Identify dominant dimensions of 3D hand shapes using statistical shape model and deep neural network
Yang, Y., Zhou, H., Song, Y., & Vink, P. (2021) · Applied Ergonomics · doi:10.1016/j.apergo.2021.103462 · Cited by 18
Attention Unet++ for lightweight depth estimation from sparse depth samples and a single RGB image
Zhao, T., Pan, S., Gao, W., Sheng, C., Sun, Y., & Wei, J. (2021) · The Visual Computer · doi:10.1007/s00371-021-02092-8 · Cited by 16
Gonzalez-Carabarin, L., Huijben, I. A. M., Veeling, B., Schmid, A., & van Sloun, R. J. G. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3176809 · Cited by 18
Automatic tracking of the dairy goat in the surveillance video
Su, Q., Tang, J., Zhai, J., Sun, Y., & He, D. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106254 · Cited by 17
GammaNet: An intensity-invariance deep neural network for computer-aided brain tumor segmentation
Huang, Z. R., Liu, Y., Song, G., et al. (2021) · Optik · doi:10.1016/j.ijleo.2021.167441 · Cited by 16
Hu, F., Wang, L., Hu, Y., Wang, D., Wang, W., Jiang, J., Li, N., & Yin, P. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab301 · Cited by 16
Chai, E., Pilancı, M., & Murmann, B. (2020) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf51394.2020.9443275 · Cited by 18
FCNN: An Efficient Intrusion Detection Method Based on Raw Network Traffic
Wang, Y., Jiang, Y., & Lan, J. (2021) · Security and Communication Networks · doi:10.1155/2021/5533269 · Cited by 17
Leading indicators for US house prices: New evidence and implications for EU financial risk managers
Basse, T., Kunze, F., Gonzalez, M. R., et al. (2021) · European Financial Management · doi:10.1111/eufm.12325 · Cited by 16
ATTICA: A Dataset for Arabic Text-Based Traffic Panels Detection
Boujemaa, K. S., Akallouch, M., Berrada, I., Fardousse, K., & Bouhoute, A. (2021) · IEEE Access · doi:10.1109/access.2021.3092821 · Cited by 17
CNN-SVM based vehicle detection for UAV platform
Valappil, N. K., & Memon, Q. A. (2021) · International Journal of Hybrid Intelligent Systems · doi:10.3233/his-210003 · Cited by 17
A Comprehensive Analysis of Low-Impact Computations in Deep Learning Workloads
Li, H., Wang, Z., Yue, X., et al. (2021) · ACM Great Lakes Symposium on VLSI (GLSVLSI) · doi:10.1145/3453688.3461747 · Cited by 16
A Convolutional Neural Network Based on Grouping Structure for Scene Classification
Wu, X., Zhang, Z., Zhang, W., et al. (2021) · Remote Sensing · doi:10.3390/rs13132457 · Cited by 16
Low‐Voltage Electrochemical LixWO3 Synapses with Temporal Dynamics for Spiking Neural Networks
Wan, Q., Rasetto, M., Sharbati, M. T., Erickson, J. R., Velagala, S. R., Reilly, M. T., Li, Y., Benosman, R. B., & Xiong, F. (2021) · Advanced Intelligent Systems · doi:10.1002/aisy.202100021 · Cited by 16
Predictive analytics and machine learning for medical informatics: A survey of tasks and techniques
Lamba, D., Hsu, W. H., & Alsadhan, M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821777-1.00023-9 · Cited by 27
Multiscale IOU: A Metric for Evaluation of Salient Object Detection with Fine Structures
Ahmadzadeh, A., Kempton, D., Chen, Y., et al. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506337 · Cited by 16
Wei, C., & Huang, T.-H. (2021) · Sensors · doi:10.3390/s21124200 · Cited by 16
NFCNN: toward a noise fusion convolutional neural network for image denoising
Xu, M., & Xie, X. (2021) · Signal Image and Video Processing · doi:10.1007/s11760-021-01965-8 · Cited by 16
Mazurek, P., & Hachaj, T. (2021) · Sensors · doi:10.3390/s21144734 · Cited by 17
Design Smells in Deep Learning Programs: An Empirical Study
Nikanjam, A., & Khomh, F. (2021) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsme52107.2021.00036 · Cited by 19
FitMe: A Fitness Application for Accurate Pose Estimation Using Deep Learning
Agarwal, S., Gupta, M., Khandelwal, S., Jain, P., Aggarwal, A., Singh, D., & Mishra, V. K. (2021) · International Conference on Secure Cyber Computing and Communications (ICSCCC) · doi:10.1109/icsccc51823.2021.9478168 · Cited by 16
Ali, M. J., Raza, B., & Shahid, A. R. (2021) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-021-00486-7 · Cited by 16
Foroughi, F., Wang, J., Nemati, A., Chen, Z., & Pei, H. (2021) · IEEE Access · doi:10.1109/access.2021.3097893 · Cited by 16
Model for the Identification and Classification of Partially Damaged and Vandalized Traffic Signs
Trpković, A., Šelmić, M., & Jevremović, S. (2021) · KSCE Journal of Civil Engineering · doi:10.1007/s12205-021-1796-9 · Cited by 16
Label Disentangled Analysis for unsupervised visual domain adaptation
Xiao, N., Zhang, L., Xu, X., Guo, T., & Ma, H. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107309 · Cited by 14
NP-CGRA: Extending CGRAs for Efficient Processing of Light-weight Deep Neural Networks
Lee, J., & Lee, J. (2021) · Design, Automation, and Test in Europe · doi:10.23919/date51398.2021.9474256 · Cited by 17
Adaptive Component Discrimination Network for Airplane Detection in Remote Sensing Images
Jia, H., Guo, Q. J., Chen, J., Wang, F., Wang, H., & Xu, F. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3098296 · Cited by 16
MRSCAtt: A Spatio-Channel Attention-Guided Network for Mars Rover Image Classification
Chakravarthy, A. S., Roy, R., & Ravirathinam, P. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00224 · Cited by 16
Deep cross feature adaptive network for facial emotion classification
Reddy, A. H., Kolli, K., & Kiran, Y. L. (2021) · Signal Image and Video Processing · doi:10.1007/s11760-021-01941-2 · Cited by 16
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
