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 64,001–64,100 of 108,435
Fusing Deep Quick Response Code Representations Improves Malware Text Classification
Ravikiran, M., & Madgula, K. (2019) · doi:10.1145/3326459.3329166 · Cited by 6
Jansen, C., Schilling, B., Strohmenger, K., Witt, M., Annuscheit, J., & Krefting, D. (2019) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid.2019.00080 · Cited by 6
A Generic Machine Learning Based Approach for Addressee Detection In Multiparty Interaction
Malik, M. U., Barange, M., Ghannad, N., et al. (2019) · ACM International Conference on Intelligent Virtual Agents (IVA) · doi:10.1145/3308532.3329462 · Cited by 6
Deconstructing multimodality: visual properties and visual context in human semantic processing
Davis, C. M., Bulat, L., Verő, A. L., & Shutova, E. (2019) · Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/s19-1013 · Cited by 6
The Role of the Input in Natural Language Video Description
Cascianelli, S., Costante, G., Devo, A., Ciarfuglia, T. A., Valigi, P., & Fravolini, M. L. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2924598 · Cited by 6
Understanding Cross-Cultural Visual Food Tastes with Online Recipe Platforms
Zhang, Q., Trattner, C., Ludwig, B., et al. (2019) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v13i01.3270 · Cited by 6
News2meme: An Automatic Content Generator from News Based on Word Subspaces from Text and Image
Shimomoto, E. K., de Souza, L. S., Gatto, B. B., et al. (2019) · doi:10.23919/mva.2019.8757876 · Cited by 6
Ferrari, C., Berretti, S., & Bimbo, A. D. (2019) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2019.8756604 · Cited by 6
Quick roughness evaluation of cut edges using a convolutional neural network
Stahl, J., & Jauch, C. (2019) · doi:10.1117/12.2519440 · Cited by 6
3D modelling of a frame assembly using deep learning and the Chu–Liu–Edmonds Algorithm
Cao, H., Mo, R., & Wan, N. (2019) · Assembly Automation · doi:10.1108/aa-08-2018-0121 · Cited by 6
Enhanced Subtraction Image Guided Convolutional Neural Network for Coronary Artery Segmentation
Fan, J., Du, C., Song, S., et al. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-9917-6_59 · Cited by 6
Classification of Radio Galaxy Images with Semi-supervised Learning
Ma, Z., Zhu, J., Zhu, Y., & Xu, H. (2019) · Communications in computer and information science · doi:10.1007/978-981-32-9563-6_20 · Cited by 7
An Element Sensitive Saliency Model with Position Prior Learning for Web Pages
Gu, Y., Chang, J., Zhang, Y., et al. (2019) · International Conference on Innovation in Artificial Intelligence (ICIAI) · doi:10.1145/3319921.3319932 · Cited by 6
Deep compressive macroscopic fluorescence lifetime imaging
Yao, R., Ochoa, M., Intes, X., & Yan, P. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363718 · Cited by 8
A Framework for Design Identification on Heritage Objects
Zhou, J., Lu, Y., Smith, K. Y., et al. (2019) · Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) · doi:10.1145/3332186.3332190 · Cited by 6
MFNN: Position and Attitude Measurement Neural Network Based on Multi-Feature Fusion
Man, J., Li, G., Xi, M., Lei, Y., Lu, W. B., & Gao, X. (2019) · IEEE Access · doi:10.1109/access.2019.2933878 · Cited by 6
Partial face recognition: a survey
Shafin, M., Hansda, R., Pallavi, E., Kumar, D., Bhattacharyya, S., & Kumar, S. (2019) · International Conference on Advanced Informatics for Computing Research (ICAICR) · doi:10.1145/3339311.3339343 · Cited by 6
Smart Phone-Based Intelligent Invoice Classification Method Using Deep Learning
Sun, Y., Zhang, J., Meng, Y., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2933527 · Cited by 6
Deep Learning Based Semantic Segmentation Applied to Satellite Image
Barthakur, M., & Sarma, K. K. (2019) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-25797-2_4 · Cited by 6
A Hybrid Approach for Artwork Recommendation
Gatti, I. (2019) · IEEE International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) · doi:10.1109/aike.2019.00055 · Cited by 6
A Pulse-gated, Neural Implementation of the Backpropagation Algorithm
Sornborger, A. T., Tao, L., Snyder, J. A., & Zlotnik, A. V. (2019) · Neuro Inspired Computational Elements Conference (NICE) · doi:10.1145/3320288.3320305 · Cited by 5
Semantic three-stream network for social relation recognition
Yan, H., & Song, C. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.08.015 · Cited by 6
Instance Segmentation by Learning Deep Feature in Embedding Space
Shang, C., Wu, Q., Meng, F., & Xu, L. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803021 · Cited by 10
Error Back Propagation Algorithm with Adaptive Learning Rate
Kolbusz, J., Różycki, P., Lysenko, O. M., & Wilamowski, B. M. (2019) · International Conference on Information and Digital Technologies (IDT) · doi:10.1109/dt.2019.8813440 · Cited by 6
Visual and Textual Sentiment Analysis of Daily News Social Media Images by Deep Learning
Felicetti, A., Martini, M., Paolanti, M., Pierdicca, R., Frontoni, E., & Zingaretti, P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30642-7_43 · Cited by 6
Explainable Anomaly Detection via Feature-Based Localization
Kitamura, S., & Nonaka, Y. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30493-5_41 · Cited by 8
Reinforcement Fuzzy Tree: A Method extracting Rules from Reinforcement Learning Models
Wu, W., & Liao, M. (2019) · IEEE/ACIS International Conference on Computer and Information Science (ICIS) · doi:10.1109/icis46139.2019.8940165 · Cited by 7
Convolutional neural network (CNNs) based image diagnosis for failure analysis of power devices
Watanabe, A., Hirose, N., Kim, H. S., et al. (2019) · Microelectronics Reliability · doi:10.1016/j.microrel.2019.113399 · Cited by 6
Propagation Mechanism for Deep and Wide Neural Networks
Xu, D., Lee, M. L., & Hsu, W. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00944 · Cited by 7
Waste Sorting Neural Network Architecture Optimization
Akhmetzyanov, K., & Yuzhakov, A. A. (2019) · International Russian Automation Conference (RusAutoCon) · doi:10.1109/rusautocon.2019.8867749 · Cited by 6
Convolutional Neural Network for Fault Diagnosis of High-Speed Train Bogie
Huang, C., Qin, N., Huang, D., & Liang, K. (2019) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2019.8866446 · Cited by 6
Towards Optimal CNN Descriptors for Large-Scale Image Retrieval
Gu, Y., Li, C., & Jiang, Y. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3351081 · Cited by 6
Enhanced Approach for Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using CNN
Tejaswini, S. V. L. L., Mittal, B., Oh, J., Tavanapong, W., Wong, J., & de Groen, P. C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33723-0_3 · Cited by 6
Fusing Handcrafted and Contextual Features for Human Activity Recognition
Vernikos, I., Mathe, E., Evaggelos Spyrou, A., Mitsou, A., Giannakopoulos, T., & Phivos Mylonas, B. (2019) · International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) · doi:10.1109/smap.2019.8864848 · Cited by 8
Mirchandani, N., & Shrivastava, A. (2019) · Conference proceedings · doi:10.1109/mwscas.2019.8884997 · Cited by 6
Deep Autoencoder-Derived Features Applied in Virtual Flow Metering for Sucker-Rod Pumping Wells
Peng, Y., Zhang, J., Xiong, C., et al. (2019) · SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition · doi:10.2118/196288-ms · Cited by 6
Physics-Aware 3D Mesh Synthesis
Wang, J., & He, Y. (2019) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2019.00062 · Cited by 8
Remote Sensing Image Retrieval Based on Semi-Supervised Deep Hashing Learning
Tang, X., Liu, C., Zhang, X., et al. (2019) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2019.8898676 · Cited by 6
Ya, Y., Pan, H. C., Jing, Z., Ren, X., & Qiao, L. (2019) · Aerospace Systems · doi:10.1007/s42401-019-00033-x · Cited by 6
Multi-Scale Machine Learning for the Classification of Building Property Values
Helber, P., Bischke, B., Guo, Q., Hees, J., & Dengel, A. R. (2019) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2019.8900257 · Cited by 6
Sirojan, T., Phung, B. T., & Eliathamby Ambikairajah, A. (2018) · International Conference on Information and Automation for Sustainability (ICIAfS) · doi:10.1109/iciafs.2018.8913352 · Cited by 6
A Story-Telling Social Robot with Emotion Recognition Capabilities for the Intellectually Challenged
Azuar, D., Gallud, G., Escalona, F., Gomez‐Donoso, F., & Cazorla, M. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-36150-1_49 · Cited by 7
Overview of Deep Learning Research
Liu, Y., & Chen, Y. (2019) · Annual International Conference on Social Science and Contemporary Humanity Development (SSCHD) · doi:10.2991/sschd-19.2019.124 · Cited by 6
Multi-branch Body Region Alignment Network for Person Re-identification
Fang, H., Chen, J., & Tian, Q. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-37731-1_28 · Cited by 6
Video Smoke Detection Based on Convolution Neural Network
Zhang, Y., & Hu, Y. (2017) · International Conference on Computer Technology, Electronics and Communication (ICCTEC) · doi:10.1109/icctec.2017.00293 · Cited by 6
Body Condition Score (BCS) Classification with Deep Learning
Çevi̇k, K. K., & Boğa, M. (2019) · Innovations in Intelligent Systems and Applications Conference (ASYU) · doi:10.1109/asyu48272.2019.8946405 · Cited by 6
Pre-processing Effects of the Tuberculosis Chest X-Ray Images on Pre-trained CNNs: An Investigation
Taşçı, E. (2020) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-36178-5_48 · Cited by 7
Image-based Kinship Verification using Fusion Convolutional Neural Network
Rachmadi, R. F., Purnama, I. K. E., Nugroho, S. M. S., et al. (2019) · IEEE International Workshop on Computational Intelligence and Applications (IWCIA) · doi:10.1109/iwcia47330.2019.8955092 · Cited by 6
Visual-Guided Robot Arm Using Multi-Task Faster R-CNN
Le, P.-P., Nguyen, V.-T., Guo, S., Tu, C., & Lien, J.-J. J. (2019) · Conference on Technologies and Applications of Artificial Intelligence (TAAI) · doi:10.1109/taai48200.2019.8959938 · Cited by 7
Performance Analysis of Using Feature Fusion for Crack Detection in Images of Historical Buildings
Elhariri, E., El-Bendary, N., & Taie, S. A. (2019) · International Conference on Management of Digital EcoSystems (MEDES) · doi:10.1145/3297662.3365800 · Cited by 6
Dynamic Hand Gesture Recognition for Robot Arm Teaching based on Improved LRCN Model
Luan, K., & Matsumaru, T. (2019) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio49542.2019.8961787 · Cited by 6
Domain Wall Memory-Based Design of Deep Neural Network Convolutional Layers
Chung, J., Choi, W., Park, J., & Ghosh, S. (2020) · IEEE Access · doi:10.1109/access.2020.2968081 · Cited by 6
An Exceedingly Fast Model for Low Resolution Handwritten Digit String Recognition
Ma, Y., Guo, J. H., & Wei, W. (2019) · IEEE International Conference on Computer Science and Network Technology (ICCSNT) · doi:10.1109/iccsnt47585.2019.8962475 · Cited by 6
A Cascaded Deep Learning Framework for Real-time and Robust Grasp Planning
Yu, H., Lai, Q., Liang, Y., Wang, Y., & Xiong, R. (2019) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio49542.2019.8961531 · Cited by 6
Geometrically Mappable Image Features
Thoma, J., Paudel, D. P., Chhatkuli, A., & Gool, L. V. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2970616 · Cited by 9
Training Effective Model for Real-Time Detection of NSFW Photos and Drawings
Zhelonkin, D., & Karpov, N. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-39575-9_31 · Cited by 8
Maestro-Watson, D., Balzategui, J., Eciolaza, L., & Arana-Arexolaleiba, N. (2020) · Journal of Electronic Imaging · doi:10.1117/1.jei.29.4.041007 · Cited by 6
Deep Convolutional Adversarial Network-Based Feature Learning for Tea Clones Identifications
Suryawati, E., Zilvan, V., Yuwana, R. S., Heryana, A., Rohdiana, D., & Pardede, H. F. (2019) · International Conference on Informatics and Computational Sciences (ICICoS) · doi:10.1109/icicos48119.2019.8982416 · Cited by 8
CBFNet: Constraint balance factor for semantic segmentation
Xu, H., Gao, Y., Li, J., & Gao, X. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.02.039 · Cited by 6
Region intensity complexity active contours
Li, X., Liu, H., & Yang, X. (2020) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-020-00704-5 · Cited by 6
Hindi Handwritten Character Recognition using Deep Convolution Neural Network
Chaudhary, D., & Sharma, K. K. (2019) · International Conference on Computing for Sustainable Global Development · Cited by 9
Malaria Detection on Giemsa-Stained Blood Smears Using Deep Learning and Feature Extraction
Dang, N., Saraf, V., Khanna, A. K., et al. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-1286-5_70 · Cited by 6
Modelling a Spatial-Motion Deep Learning Framework to Classify Dynamic Patterns of Videos
Arachchi, S. P. K., Shih, T. K., & Hakim, N. L. (2020) · Applied Sciences · doi:10.3390/app10041479 · Cited by 6
Negative correlation learning in the extreme learning machine framework
Perales-González, C., Carbonero-Ruz, M., Pérez-Rodríguez, J., Becerra‐Alonso, D., & Fernández‐Navarro, F. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-04788-9 · Cited by 7
Forged Multinational Currency Identification and Detection System using Deep Learning Algorithm
Jadhav, M., Sharma, Y. K., & M., G. (2020) · International Journal of Computer Applications · doi:10.5120/ijca2020919970 · Cited by 6
Image Aesthetics Assessment Using Multi Channel Convolutional Neural Networks
Doshi, N., Shikkenawis, G., & Mitra, S. K. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-4018-9_2 · Cited by 7
Comparison between handwritten word and speech record in real-time using CNN architectures
Pinzon-Arenas, J. O., & Moreno, R. J. (2020) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v10i4.pp4313-4321 · Cited by 6
Local-Global Feature for Video-Based One-Shot Person Re-Identification
Zhao, C., Zhang, Z., Yan, J., et al. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053134 · Cited by 6
Plants recognition using embedded Convolutional Neural Networks on Mobile devices
Pechebovicz, D., Premebida, S. M., Soares, V. R., et al. (2020) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit45562.2020.9067289 · Cited by 6
Regularised Encoder-Decoder Architecture for Anomaly Detection in ECG Time Signals
Chandra, A., & Kala, R. (2019) · IEEE Conference on Information and Communication Technology (CICT) · doi:10.1109/cict48419.2019.9066261 · Cited by 7
Li, K., Shi, D., Zhang, Y., et al. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.090 · Cited by 6
Breve, F., & Fischer, C. N. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207606 · Cited by 7
Verma, M., Bhui, J. K., Vipparthi, S. K., et al. (2018) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/3293353.3293374 · Cited by 6
Jiang, Y., Xu, J., & Zhang, T. (2020) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-020-01132-4 · Cited by 6
McManigle, J. E., Bartz, R. R., & Carin, L. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9176334 · Cited by 7
Epileptic Seizure Detection using Multicolumn Convolutional Neural Network
Bhattacherjee, I. (2020) · International Conference on Computing for Sustainable Global Development · doi:10.23919/indiacom49435.2020.9083698 · Cited by 6
A Scene-to-Speech Mobile based Application: Multiple Trained Models Approach
Karkar, A., Kunhoth, J., & Al-Maadeed, S. A. (2020) · IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) · doi:10.1109/iciot48696.2020.9089557 · Cited by 6
Analyzing AIA Flare Observations Using Convolutional Neural Networks
Love, T., Neukirch, T., & Parnell, C. E. (2020) · Frontiers in Astronomy and Space Sciences · doi:10.3389/fspas.2020.00034 · Cited by 14
A Pulse-width Modulation Neuron with Continuous Activation for Processing-In-Memory Engines
Zhang, S., Li, B., Li, H. H., et al. (2020) · Design, Automation, and Test in Europe · doi:10.23919/date48585.2020.9116323 · Cited by 6
Partial domain adaptation based on shared class oriented adversarial network
Qiu, W., Chen, W., & Hu, H. (2020) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2020.103018 · Cited by 6
PCANN: Distributed ANN Architecture for Image Recognition in Resource-Constrained IoT Devices
Bi, T., Liu, Q., Özçelebi, T., et al. (2019) · International Conference on Intelligent Environments (IE) · doi:10.1109/ie.2019.000-3 · Cited by 6
Boosting binary masks for multi-domain learning through affine transformations
Mancini, M., Ricci, E., Barbara Caputo, B., & Bulò, S. R. (2020) · Machine Vision and Applications · doi:10.1007/s00138-020-01090-5 · Cited by 6
Towards dense people detection with deep learning and depth images
Fuentes-Jiménez, D., Losada-Gutiérrez, C., Casillas-Pérez, D., Macías-Guarasa, J., Pizarro, D., López, R. M., & Luna, C. A. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104484 · Cited by 6
Real-time tracking based on deep feature fusion
Pang, Y., Li, F., Qiao, X., & Gilman, A. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09267-w · Cited by 5
Hallucinating Saliency Maps for Fine-grained Image Classification for Limited Data Domains
Figueroa-Flores, C., Raducanu, B., Berga, D., & van de Weijer, J. (2021) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0010299501630171 · Cited by 6
Deep Learning-Based Ship Detection in Remote Sensing Imagery Using TensorFlow
Apoorva, A., Mishra, G. K., Sahoo, R. R., Bhoi, S. K., & Mallick, C. (2020) · Algorithms for intelligent systems · doi:10.1007/978-981-15-5243-4_14 · Cited by 7
On Multiview Robustness of 3D Adversarial Attacks
Yao, P., So, A., Chen, T., & Ji, H. (2020) · Practice and Experience in Advanced Research Computing · doi:10.1145/3311790.3396652 · Cited by 6
3D Object Classification Using 2D Perspectives of Point Clouds
Jansen, L., Liebrecht, N., Soltaninejad, S., & Basu, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-54407-2_38 · Cited by 6
Chitty-Venkata, K. T., & Somani, A. K. (2020) · IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP) · doi:10.1109/asap49362.2020.00016 · Cited by 6
Fate of Duplicated Neural Structures
Seoane, L. F. (2020) · Entropy · doi:10.3390/e22090928 · Cited by 7
Deep Neural Framework With Visual Attention and Global Context for Predicting Image Aesthetics
Xu, Y., Zhang, N., Wei, P., Sang, G., Li, L., & Yuan, F. (2020) · IEEE Access · doi:10.1109/access.2020.3015060 · Cited by 6
Convolutional Deep Learning Network for Handwritten Arabic Script Recognition
Elleuch, M., & Kherallah, M. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-49336-3_11 · Cited by 6
Millham, R. C., Agbehadji, I. E., & Yang, H. (2020) · Springer tracts in nature-inspired computing · doi:10.1007/978-981-15-6695-0_2 · Cited by 7
On-chip Memory Optimized CNN Accelerator with Efficient Partial-sum Accumulation
Xu, H., Shiomi, J., & Onodera, H. (2020) · ACM Great Lakes Symposium on VLSI (GLSVLSI) · doi:10.1145/3386263.3406925 · Cited by 6
Research of Neural Network Structural Optimization Based on Information Entropy
Wang, D., & Shao, F. (2020) · Chinese Journal of Electronics · doi:10.1049/cje.2020.05.006 · Cited by 6
Blind compression artifact reduction using dense parallel convolutional neural network
Amaranageswarao, G., Deivalakshmi, S., & Ko, S. (2020) · Signal Processing Image Communication · doi:10.1016/j.image.2020.116009 · Cited by 6
Convolutional Neural Network with Multi-Valued Neurons
Aizenberg, I. N., & Vasko, A. (2020) · IEEE International Conference on Data Stream Mining & Processing (DSMP) · doi:10.1109/dsmp47368.2020.9204076 · Cited by 8
Accelerating Deep Neural Network Computation on a Low Power Reconfigurable Architecture
Xiong, Y., Zhou, J., Pal, S., Blaauw, D. T., Kim, H. S., Mudge, T., Dreslinski, R., & Chakrabarti, C. (2020) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas45731.2020.9180871 · Cited by 6
Exploring Highly Efficient Compact Neural Networks For Image Classification
Xie, X., Zhou, Y., & Kung, S. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9191334 · Cited by 6
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
