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 57,501–57,600 of 108,439
Modelling of an AUV with Voith-Schneider vector thruster
Mishra, R., & Chitre, M. (2016) · IEEE/OES Autonomous Underwater Vehicles · doi:10.1109/auv.2016.7778696 · Cited by 8
A computational approach to relative aesthetics
Gattupalli, V., Chandakkar, P. S., & Li, B. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900003 · Cited by 8
Xu, W. H., He, J., Zhang, H., et al. (2016) · doi:10.1145/2996890.3007881 · Cited by 9
Choi, Y., Kim, H.-I., & Ro, Y. M. (2016) · Electronic Imaging · doi:10.2352/issn.2470-1173.2016.11.imawm-470 · Cited by 8
Convolutional Neural Networks for Robust and Real-Time 2-D/3-D Registration
Miao, S., Wang, J. Z., & Liao, R. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-810408-8.00016-x · Cited by 9
Exploiting generic multi-level convolutional neural networks for scene understanding
Nguyen, T., Liu, L., & Nguyen, K. T. T. M. (2016) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv.2016.7838719 · Cited by 9
What makes for good multiple object trackers?
Zhang, Y., Huang, Y., & Wang, L. (2016) · IEEE International Conference on Digital Signal Processing (DSP) · doi:10.1109/icdsp.2016.7868601 · Cited by 7
Classification of foods by transferring knowledge from ImageNet dataset
Heravi, E. J., Aghdam, H. H., & Puig, D. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2268737 · Cited by 8
Multiple-Branches Faster RCNN for Human Parts Detection and Pose Estimation
Wei, K., & Zhao, X. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54526-4_33 · Cited by 8
SAR Automatic Target Recognition Based on Deep Convolutional Neural Networks
Zhan, R., Tian, Z., Hu, J., et al. (2017) · DEStech Transactions on Computer Science and Engineering · doi:10.12783/dtcse/aita2016/7564 · Cited by 8
Giannakidis, A., Kamnitsas, K., Spadotto, V., et al. (2016) · International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) · doi:10.1109/sitis.2016.16 · Cited by 8
Visual Background Recommendation for Dance Performances Using Dancer-Shared Images
Wen, J., Li, X., She, J. P. M., Park, S., & Cheung, M. (2016) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/ithings-greencom-cpscom-smartdata.2016.120 · Cited by 8
Deep Fisher Discriminant Analysis
Díaz–Vico, D., Omari, A., Torres‐Barrán, A., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59147-6_43 · Cited by 8
Pedestrian detection based on multi-convolutional features by feature maps pruning
Rui, T., Zou, J., Zhou, Y., Fang, H., & Gao, Q. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4837-0 · Cited by 8
A novel multi-target track initiation method based on convolution neural network
Zhang, Y., Yang, S., Li, H., & Mu, H. (2017) · International Workshop on Remote Sensing with Intelligent Processing (RSIP) · doi:10.1109/rsip.2017.7958813 · Cited by 10
Movie Recommendation Based on Visual Features of Trailers
Fan, Y., Wang, Y., Huan, Y., & Liu, B. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-61542-4_23 · Cited by 8
A Comprehensive Study of Group Activity Recognition Methods in Video
Vahora, S., & Chauhan, N. C. (2017) · Indian Journal of Science and Technology · doi:10.17485/ijst/2017/v10i23/113996 · Cited by 8
Combining Deep Learning and Structured Prediction for Segmenting Masses in Mammograms
Dhungel, N., Carneiro, G., & Bradley, A. P. (2017) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-42999-1_13 · Cited by 8
Fully Convolutional Networks in Medical Imaging: Applications to Image Enhancement and Recognition
Baumgartner, C. F., Oktay, O., & Rueckert, D. (2017) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-42999-1_10 · Cited by 8
Challenges and Opportunities in Automated Detection of Eating Activity
Thomaz, E., Essa, I. A., & Abowd, G. D. (2017) · Springer eBooks · doi:10.1007/978-3-319-51394-2_9 · Cited by 10
Pedestrian near-miss analysis on vehicle-mounted driving recorders
Suzuki, T., Aoki, Y., & Kataoka, H. (2017) · Machine Vision Applications · doi:10.23919/mva.2017.7986889 · Cited by 8
Deep visual words: Improved fisher vector for image classification
Diba, A., Pazandeh, A. M., & Gool, L. V. (2017) · Machine Vision Applications · doi:10.23919/mva.2017.7986832 · Cited by 10
Incorporating side-channel information into convolutional neural networks for robotic tasks
Zhou, Y., & Hauser, K. K. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989251 · Cited by 11
PKU-ICST at TRECVID 2014: Instance Search Task.
Peng, Y., Huang, X., Qi, J., et al. (2013) · TRECVID · Cited by 8
Kalanit Grill-spector, A., Kay, K., & Weiner, K. S. (2017) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-61657-5_1 · Cited by 8
Jin, B., Yang, J., Huang, X., & Khan, D. (2017) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1145/3106426.3109426 · Cited by 8
Distributed learning of deep feature embeddings for visual recognition tasks
Bhattacharjee, B., Hill, M. L., Wu, H., Chandakkar, P. S., Smith, J. R., & Wegman, M. N. (2017) · IBM Journal of Research and Development · doi:10.1147/jrd.2017.2706118 · Cited by 8
Conditional preference nets for user and item cold start problems in music recommendation
Chou, S.-Y., Yang, L.-C., Yang, Y., et al. (2017) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2017.8019472 · Cited by 8
Telepath: Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems
Wang, Y., Xu, J., Wu, A., Li, M., He, Y., Hu, J., & Yan, W. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11243 · Cited by 10
Poster abstract: predicting heating energy demand by computer vision
Despotović, M., Sakeena, M., Koch, D., et al. (2017) · Computer Science - Research and Development · doi:10.1007/s00450-017-0363-6 · Cited by 8
Yang, K., Wang, S., Zhou, J., et al. (2017) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2017.8050800 · Cited by 8
Street view image classification based on convolutional neural network
Wang, Q., Zhou, C., & Xu, N. (2017) · IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) · doi:10.1109/iaeac.2017.8054251 · Cited by 8
RGB-D data fusion in complex space
Cai, Z., & Shao, L. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296625 · Cited by 8
CTC Network with Statistical Language Modeling for Action Sequence Recognition in Videos
Lin, M., Inoue, N., & Shinoda, K. (2017) · Thematic Workshops of ACM Multimedia · doi:10.1145/3126686.3126755 · Cited by 7
Deep Learning Features for Lung Adenocarcinoma Classification with Tissue Pathology Images
He, J., Shang, L., Ji, H., & Zhang, X. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70093-9_79 · Cited by 10
Automatic MOOC Video Classification using Transcript Features and Convolutional Neural Networks
Chatbri, H., McGuinness, K., Little, S., et al. (2017) · doi:10.1145/3132390.3132393 · Cited by 9
Large Scale Image Classification Based on CNN and Parallel SVM
Sun, Z., Li, F., & Huang, H. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70087-8_57 · Cited by 8
Qin, J., Han, C., Bai, C., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-69923-3_24 · Cited by 8
An Efficient Task-based All-Reduce for Machine Learning Applications
Li, Z., Davis, J., & Jarvis, S. A. (2017) · doi:10.1145/3146347.3146350 · Cited by 9
Improving the lenet with batch normalization and online hard example mining for digits recognition
Xie, Y., Jin, H., & Tsang, E. C. C. (2017) · International Conference on Wavelet Analysis and Pattern Recognition · doi:10.1109/icwapr.2017.8076680 · Cited by 8
Multi-modality recognition of human face and ear based on deep learning
Fan, T., Mu, Z., & Yang, R. (2017) · International Conference on Wavelet Analysis and Pattern Recognition · doi:10.1109/icwapr.2017.8076660 · Cited by 8
A Novel Method for Ship Detection and Classification on Remote Sensing Images
Liu, Y., Cui, H., & Li, G. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68612-7_63 · Cited by 8
An autopilot system based on ROS distributed architecture and deep learning
Liu, M., Niu, J., & Wang, X. (2017) · 2022 IEEE 20th International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin.2017.8104950 · Cited by 8
Murphy, J., Pao, Y., & Haque, A. (2017) · doi:10.1145/3149808.3149811 · Cited by 7
Study on semantic image segmentation based on convolutional neural network
Li, L., Qian, B., Lian, J., Zheng, W., & Zhou, Y. (2017) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-162254 · Cited by 8
Viewpoint selection for visual failure detection
Saran, A., Lakic, B., Majumdar, S., Hess, J., & Niekum, S. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206439 · Cited by 8
Li, J., Ding, W., Li, H., & Liu, C. (2017) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus.2017.8278309 · Cited by 8
Monocular semantic SLAM in dynamic street scene based on multiple object tracking
Chen, W., Fang, M., Liu, Y., & Li, L. (2017) · IEEE Conference on Cybernetics and Intelligent Systems · doi:10.1109/iccis.2017.8274845 · Cited by 8
Exploiting State-of-the-Art Deep Learning Methods for Document Image Analysis
Pondenkandath, V., Seuret, M., Ingold, R., et al. (2017) · doi:10.1109/icdar.2017.325 · Cited by 8
SAR Automatic Target Recognition Based on Deep Convolutional Neural Network
Xu, Y., Liu, K., Ying, Z., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-71598-8_58 · Cited by 8
Deep Action Parsing in Videos With Large-Scale Synthesized Data
Liu, L., Zhou, Y., & Shao, L. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2813530 · Cited by 8
Tejada, E. D., & Rodríguez, P. (2017) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2017.8309069 · Cited by 8
Zhong, W., Xiong, H., Yang, Z., & Zhang, T. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296544 · Cited by 8
How to drive optical measurement systems to outstanding performance
Haist, T. J., Osten, W., & Manske, E. (2018) · doi:10.1117/12.2300856 · Cited by 8
A complete person re-identification model using Kernel-PCA-based Gabor-filtered hybrid descriptors
Sathish, P. K., & Balaji, S. (2018) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-018-0153-3 · Cited by 8
Deng, L., Wang, Y., Liu, B., et al. (2018) · Machine Vision and Applications · doi:10.1007/s00138-018-0928-9 · Cited by 8
A robust proposal generation method for text lines in natural scene images
Fan, K., & Baek, S. J. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.03.041 · Cited by 8
THE EXTRACTION OF POST-EARTHQUAKE BUILDING DAMAGE INFORMATIOM BASED ON CONVOLUTIONAL NEURAL NETWORK
Chen, M., Wang, X., Dou, A., et al. (2018) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-3-161-2018 · Cited by 8
Robust Fingerprint Matching Based on Convolutional Neural Networks
Zhu, Y., Yin, X., & Hu, J. (2018) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-90775-8_5 · Cited by 8
Residual convolutional neural network for diabetic retinopathy
Rufaida, S. I., & Fanany, M. I. (2017) · International Conference on Advanced Computer Science and Information Systems · doi:10.1109/icacsis.2017.8355060 · Cited by 8
Scene Semantic Recognition Based on Probability Topic Model
Feng, J., & Fu, A. (2018) · Information · doi:10.3390/info9040097 · Cited by 8
Hybrid generative–discriminative approach to age-invariant face recognition
Sajid, M., & Shafique, T. (2018) · Journal of Electronic Imaging · doi:10.1117/1.jei.27.2.023029 · Cited by 8
Detection of Mechanical Damages in Sawn Timber Using Convolutional Neural Networks
Rudakov, N., Eerola, T., Lensu, L. T., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-12939-2_9 · Cited by 8
Bibliometric Analysis of the Deep Learning Research Status with the Data from Web of Science
Mao, M., Li, Z., Zhao, Z., & Zeng, L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93803-5_55 · Cited by 9
Vieira, R. T., Negri, T. T., & Gonzaga, A. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6204-1 · Cited by 8
Local Fast R-CNN Flow for Object-Centric Event Recognition in Complex Traffic Scenes
Gu, Q., Yang, J., Yan, W. Q., Li, Y., & Klette, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-92753-4_34 · Cited by 9
Gabriel, E., Schleiss, M., Schramm, H., et al. (2018) · Journal of Electronic Imaging · doi:10.1117/1.jei.27.5.051228 · Cited by 8
Deterministic Binary Filters for Convolutional Neural Networks
Tseng, V. W.-S., Bhattacharya, S., Fernández-Marqués, J., Alizadeh, M., Tong, C., & Lane, N. D. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/380 · Cited by 9
Nrityamanthan: Unravelling the Intent of the Dancer Using Deep Learning
Mohanty, A., Roy, K., & Sahay, R. R. (2018) · Springer eBooks · doi:10.1007/978-981-10-7221-5_11 · Cited by 9
Deep Learning Models for Predicting Phenotypic Traits and Diseases from Omics Data
Islam, M. M., Wang, Y., & Hu, P. (2018) · InTech eBooks · doi:10.5772/intechopen.75311 · Cited by 9
Surgical workflow image generation based on generative adversarial networks
Chen, Y.-W., Zhong, K., Wang, F., Wang, H., & Zhao, X. (2018) · International Conference on Artificial Intelligence and Big Data (ICAIBD) · doi:10.1109/icaibd.2018.8396171 · Cited by 9
Object-Centric Scene Understanding for Image Memorability Prediction
Yoon, S., & Kim, J.-P. (2018) · IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr.2018.00070 · Cited by 8
Zhao, J., Li, J., & Ma, Y. (2018) · International Conference on Computer Technology Applications (ICCTA) · doi:10.1109/cata.2018.8398672 · Cited by 8
Face re-identification in thermal infrared spectrum based on ThermalFaceNet neural network
Grudzień, A., Kowalski, M., & Pałka, N. (2018) · International Conference on Microwaves, Radar & Wireless Communications · doi:10.23919/mikon.2018.8405170 · Cited by 11
Smart Robotic Wheelchair for Bus Boarding Using CNN Combined with Hough Transforms
Ali, S., Mamun, S. A., Fukuda, H., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-95957-3_18 · Cited by 8
Copy-Move Forgery Localization Using Convolutional Neural Networks and CFA Features
Liu, L., Zhao, Y., Ni, R., & Tian, Q. (2018) · International Journal of Digital Crime and Forensics · doi:10.4018/ijdcf.2018100110 · Cited by 8
Automatic Motion Artifact Detection for Whole-Body Magnetic Resonance Imaging
Küstner, T., Jandt, M., Liebgott, A., Mauch, L., Martirosian, P., Bamberg, F., Nikolaou, K., Gatidis, S., Schick, F., & Yang, B. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462414 · Cited by 9
Multi-object Detection Based on Deep Learning in Real Classrooms
Shao, B., Jiang, F., & Shen, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-97310-4_40 · Cited by 8
Research on Image Semantic Segmentation Based on FCN-VGG and Pyramid Pooling Module
Hao, B., & Kang, D.-S. (2018) · The Journal of Korean Institute of Information Technology · doi:10.14801/jkiit.2018.16.7.1 · Cited by 8
SlimNets: An Exploration of Deep Model Compression and Acceleration
Oguntola, I., Olubeko, S., & Sweeney, C. (2018) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec.2018.8547604 · Cited by 12
Optimizing Deep Convolutional Neural Network for Facial Expression Recognitions
Chavan, U. B., & Kulkarni, D. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-1402-5_14 · Cited by 8
Fingerprint Pore Extraction Using Convolutional Neural Networks and Logical Operation
Zhao, Y., Liu, F., & Shen, L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-97909-0_5 · Cited by 8
General-to-specific learning for facial attribute classification in the wild
Sun, Y., & Yu, J. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.09.003 · Cited by 8
Evaluation model for business sites planning based on online and offline datasets
Chen, J., Chen, Y., & Jin, H. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.08.024 · Cited by 8
Tree-CNN: from generalization to specialization
Jiang, S., Xu, T., Guo, J., & Zhang, J. (2018) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-018-1197-z · Cited by 8
Improvement of Pruning Method for Convolution Neural Network Compression
Liu, C., & Liu, Q. (2018) · International Conference on Deep Learning Technologies (ICDLT) · doi:10.1145/3234804.3234824 · Cited by 8
Joint Learning of LSTMs-CNN and Prototype for Micro-video Venue Classification
Liu, W., Huang, X., Cao, G., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00767-6_65 · Cited by 8
Deep Belief Network for Molecular Feature Selection in Ligand-Based Virtual Screening
Nasser, M., Salim, N. B., Hamza, H., & Saeed, F. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-99007-1_1 · Cited by 8
Mohan Sai., S., Naresh, K., RajKumar, S. S., et al. (2018) · doi:10.1109/icicct.2018.8473140 · Cited by 8
PhotoSafer: Content-Based and Context-Aware Private Photo Protection for Smartphones
Li, A., Darling, D., & Li, Q. (2018) · IEEE Symposium on Privacy-Aware Computing (PAC) · doi:10.1109/pac.2018.00008 · Cited by 10
Automated Recovery of Structural Drawing Images Collected from Postdisaster Reconnaissance
Yeum, C. M., Lund, A., Dyke, S. J., et al. (2018) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000798 · Cited by 8
Learning Map-Independent Evaluation Functions for Real-Time Strategy Games
Yang, Z., & Ontañón, S. (2018) · IEEE Conference on Computational Intelligence and Games (CIG) · doi:10.1109/cig.2018.8490369 · Cited by 7
3D Semantic Mapping Based on Convolutional Neural Networks
Li, J., Liu, Y., Wang, J., Yan, M., & Yao, Y. (2018) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2018.8482938 · Cited by 8
A Novel Deep Learning Approach: Stacked Evolutionary Auto-encoder
Cai, Y., Cai, Z., Zeng, M., Liu, X., Wu, J. X., & Wang, G. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489138 · Cited by 8
Viet, S. D., & Bao, C. L. T. (2018) · Informatica · doi:10.31449/inf.v42i3.2301 · Cited by 10
dAFM: Fusing Psychometric and Connectionist Modeling for Q-matrix Refinement
Pardos, Z. A., & Dadu, A. (2018) · Journal of Educational Data Mining · doi:10.5281/zenodo.3554689 · Cited by 9
Ship Discrimination with Deep Convolutional Neural Networks in Sar Images
Wang, Y., Wang, C., & Zhang, H. (2018) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2018.8519552 · Cited by 8
Autoencoders for learning template spectrograms in electrocorticographic signals
Pailla, T., Miller, K. J., & Gilja, V. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aaf13f · Cited by 8
Crack Detection Of Concrete Surface Based On \Newline Convolutional Neural Networks
Yao, G., Wei, F., Qian, J., & Wu, Z. (2018) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2018.8527035 · Cited by 11
Malik, A. M., Lu, M., Wang, N., Lin, Y., & Yoo, S. (2018) · New York Scientific Data Summit (NYSDS) · doi:10.1109/nysds.2018.8538946 · Cited by 9
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
