The DOI matches its Crossref record, with a small difference in title or year; 31 of the 31 references it cites were found in a registry or an index; 2 of the 14 sources its claims cite were checked for support.
ImageNet classification with deep convolutional neural networks
Open accessAbstract
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
42 Own result · 21 Own interpretation · 7 No basis given · 4 No full text of the sources · 3 Sources not found · 2 Supported · 2 Not checked yet · 1 Own method
Every source the paper cites for this claim was checked and supports it.
In the paper
“This technique reduces complex co adaptations of neurons, since a neuron cannot rely on the presence of particular other neurons.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
What the sources say
- Supportedcites [12] 12. Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012). source
The paper states on p. 1 that dropout prevents complex co-adaptations by randomly omitting hidden units, so a hidden unit cannot rely on other hidden units being present. This directly supports the citing sentence. (LLM, conf=0.95)
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“There is, however, a very efficient version of model combination that only costs about a factor of two during training.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
What the sources say
- Supportedcites [12] 12. Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012). source
The paper describes dropout as an efficient way to combine many neural-network models (p. 1). In F.6 Training (p. 17), it reports that ImageNet training took roughly four days with dropout versus two days without it, supporting the approximate factor-of-two training cost. (LLM, conf=0.88)
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This scheme reduces our top-1 and top-5 error rates by 1.7% and 1.2%, respectively, as compared with a net with half as many kernels in each convolutional layer trained on one GPU.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.2. Training on multiple GPUs
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The size of our network made overfitting a significant problem, even with 1.2 million labeled training examples”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“So these data augmentation schemes are, in effect, computationally free.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 18
- Probably not a substantial claim. 26
- The quote only partly states this claim. 2
Create a free account to see these claims.
Create a free account to see these assumptions.
34 more claims
Create a free account to see all 82 claims and to verify the 3 references still open.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In the field of DL, the CNN is the most famous and commonly employed algorithm [30, 71–75].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [30]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Convolutional neural networks (CNNs), built for spatial data, have demonstrated performance comparable to that of specialists in cancer detection and tumor segmentation [15,16,17,18,19,20].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [16]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Deep CNN architectures with 8, even 22 layers [4], [33], can be useful even for CADe problems where the available training datasets are limited.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [4]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Fully convolutional neural networks (fCNNs) have provided state-of-the-art performance for object classification and image segmentation (1).”
From Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning (2018) · cites this paper as [1]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The first wave saw the emergence of deep learning as the dominant paradigm for analyzing high-dimensional biological data.”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [1]
Experimental — accuracy not yet measured
19 more claims about this paper
Create a free account to see every claim other papers make about this one.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
“we found that removing any convolutional layer (each of which contains no more than 1% of the model's parameters) resulted in inferior performance.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.5. Overall architecture
“The third convolutional layer has 384 kernels of size 3 × 3 × 256”
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,884
Self-citationLearning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,501
Self-citation(2010) · International Conference on Machine Learning · Cited by 13,995
Self-citationImproving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
Self-citationFukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759
LabelMe: A Database and Web-Based Tool for Image Annotation
Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0090-8 · Cited by 4,210
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196
Research works printed but not linked: 13
- Berg A., 2010, Large scale visual recognition challenge
- Deng J., 2009, CVPR09
- Krizhevsky A., 2009, Department of Computer Science
- Krizhevsky A., 2010, Convolutional deep belief networks on cifar-10. Unpublished manuscript
- Krizhevsky A., 2011, ESANN
- LeCun Y., 1990, Advances in Neural Information Processing Systems
- LeCun Y., 1985, Une procedure d'apprentissage pour reseau a seuil asymmetrique (a learning scheme for asymmetric threshold networks)
- 10.5555/1896300.1896315 · DOI
- Mensink T., 2012, Italy
- 10.5555/3104322.3104425 · DOI
- Rumelhart D.E., 1985, DTIC Document
- 10.5555/938980.939477 · DOI
- Werbos P., 1974, Beyond regression: New tools for prediction and analysis in the behavioral sciences
Cited by
Items 44,801–44,900 of 108,439
Cai, C., Liu, S., Wang, L., Yang, B., Zhi, M., Wang, R., & He, W. (2019) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2019.8914437 · Cited by 18
Guitar Tablature Estimation with a Convolutional Neural Network
Wiggins, A., & Kim, Y. E. (2019) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3527799 · Cited by 14
Multiscale Residual Convolution Neural Network and Sector Descriptor-Based Road Detection Method
Dai, J.-G., Du, Y., Zhu, T., Wang, Y., & Gao, L. (2019) · IEEE Access · doi:10.1109/access.2019.2956725 · Cited by 14
Long-Term Knowledge Distillation of Visual Place Classifiers
Tomoe, H., & Kanji, T. (2019) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2019.8917199 · Cited by 15
CASR: a context-aware residual network for single-image super-resolution
Wu, Y., Ji, X., Ji, W., Tian, Y. T., & Zhou, H. M. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04609-8 · Cited by 14
High-Resolution Imagery Classification Based on Different Levels of Information
Li, E., Samat, A., Liu, W., Lin, C., & Bai, X. (2019) · Remote Sensing · doi:10.3390/rs11242916 · Cited by 14
Understanding the Impact of On-chip Communication on DNN Accelerator Performance
Guirado, R., Kwon, H., Alarcón, E., Abadal, S., & Krishna, T. (2019) · IEEE International Conference on Electronics, Circuits and Systems (ICECS) · doi:10.1109/icecs46596.2019.8964858 · Cited by 14
Pedestrians' Detection Methods in Video Images: A Literature Review
Antonio, J., & Romero, M. (2018) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci46756.2018.00074 · Cited by 15
Mine-Like Object Sensing in Sonar Imagery with a Compact Deep Learning Architecture for Scarce Data
Phung, S. L., Nguyen, T. N. A., Le, H. T., Chapple, P. B., Ritz, C. H., Bouzerdoum, A., & Tran, L. C. (2019) · 2019 Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta47822.2019.8945982 · Cited by 17
Derin Öğrenme Araştırma Alanlarının Literatür Taraması
Yapıcı, M. M., Tekerek, A., & Topaloğlu, N. (2019) · Gazi Journal of Engineering Sciences · doi:10.30855/gmbd.2019.03.01 · Cited by 19
A., G. S. C., Bhowmik, N., & Breckon, T. P. (2019) · IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla.2019.00119 · Cited by 14
Deep learning model with low-dimensional random projection for large-scale image search
Alzu’bi, A., & Abuarqoub, A. (2020) · Engineering Science and Technology an International Journal · doi:10.1016/j.jestch.2019.12.004 · Cited by 16
Visual Tracking With Long-Short Term Based Correlation Filter
Yang, Y., Xing, W., Zhang, S., Gao, L., Yu, Q., Che, X., & Lu, W. B. (2020) · IEEE Access · doi:10.1109/access.2020.2968125 · Cited by 14
Hierarchical LSTM Framework for Long-Term Sea Surface Temperature Forecasting
Liu, X., Wilson, T., Tan, P., & Luo, L. (2019) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2019.00018 · Cited by 14
NF-RCNN: Heart localization and right ventricle wall motion abnormality detection in cardiac MRI
Kermani, S., Oghli, M. G., Mohammadzadeh, A., & Kafieh, R. (2020) · Physica Medica · doi:10.1016/j.ejmp.2020.01.011 · Cited by 16
Cross-Domain Transfer Learning for Complex Emotion Recognition
Nagarajan, B., & Murthy, O. V. R. (2019) · IEEE Region 10 Symposium (TENSYMP) · doi:10.1109/tensymp46218.2019.8971023 · Cited by 14
Dong, W., Li, P., Xiaoli Li, A., Wei, Z., Zhou, Y., Pei, H., Li, F., Bian, Z., Wang, L., & Yin, S. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.01.025 · Cited by 14
Automatic Caption Generation of Retinal Diseases with Self-trained RNN Merge Model
Mishra, S., & Banerjee, M. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-2930-6_1 · Cited by 14
Osanlou, K., Bursuc, A., Guettier, C., Cazenave, T., & Jacopin, É. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968113 · Cited by 15
Automated Grading of Diabetic Retinopathy in Retinal Fundus Images using Deep Learning
Hathwar, S. B., & Srinivasa, G. (2019) · IEEE International Conference on Signal and Image Processing Applications (ICSIPA) · doi:10.1109/icsipa45851.2019.8977760 · Cited by 16
3D Capsule Networks for Object Classification With Weight Pruning
Kakillioglu, B., Ren, A., Wang, Y., & Velipasalar, S. (2020) · IEEE Access · doi:10.1109/access.2020.2971950 · Cited by 14
CNN BASED DETECTION OF BUILDING ROOFS FROM HIGH RESOLUTION SATELLITE IMAGES
Hang, L., & Cai, G. (2020) · 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-w10-187-2020 · Cited by 16
Tang, W., Yang, Q., & Yan, W. (2019) · IEEE PES Asia-Pacific Power and Energy Engineering Conference · doi:10.1109/appeec45492.2019.8994713 · Cited by 14
Fast and Reliable Group Attendance Marking System Using Face Recognition In Classrooms
Pradeesh, N., Abhishek Lal, B., Padmanabhan, G., et al. (2019) · International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT) · doi:10.1109/icicict46008.2019.8993323 · Cited by 14
Face Recognition Based on The Improved MobileNet
Zhou, Y., Liu, Y., Han, G., et al. (2019) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci44817.2019.9003100 · Cited by 14
Faster R-CNN Based Indoor Flame Detection for Firefighting Robot
Guo, J., Hou, Z., Xie, X., Yao, S., Wang, Q., & Jin, X. (2019) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci44817.2019.9002843 · Cited by 15
A Classification Method of Oracle Materials Based on Local Convolutional Neural Network Framework
Chen, S., Han, X., Weize, G., et al. (2020) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2020.2973109 · Cited by 14
Deep Learning Based Preamble Detection and TOA Estimation
Sun, H., Kaya, A. Ö., Macdonald, M., Viswanathan, H., & Hong, M. (2019) · Globecom · doi:10.1109/globecom38437.2019.9013265 · Cited by 14
Road Crack Image Segmentation Using Global Context U-net
Chen, J., Liu, G., & Chen, X. (2019) · doi:10.1145/3374587.3374602 · Cited by 14
An End to End Framework With Adaptive Spatio-Temporal Attention Module for Human Action Recognition
Liu, S., Ma, X., Wu, H., & Li, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2979549 · Cited by 16
Wei, W., Zhang, L., Li, Y., Wang, C., & Zhang, Y. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.2977655 · Cited by 14
Hand Gesture Recognition Using Deep Network Through Trajectory-to-Contour Based Images
Sarma, D., & Bhuyan, M. K. (2018) · IEEE India Conference · doi:10.1109/indicon45594.2018.8987097 · Cited by 14
A Complete Bangla Optical Character Recognition System: An Effective Approach
Ahmed, T., Raihan, M. N., Kushol, R., et al. (2019) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit48885.2019.9038551 · Cited by 14
Thenmozhi, M., Saravanan, M., Kumar, K., et al. (2020) · Soft Computing · doi:10.1007/s00500-020-04801-2 · Cited by 14
RetractedCrowding in humans is unlike that in convolutional neural networks
Lonnqvist, B., Clarke, A. D. F., & Chakravarthi, R. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.03.021 · Cited by 18
Adaptive partial scanning transmission electron microscopy with reinforcement learning
Ede, J. M. (2021) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abf5b6 · Cited by 19
Radar-based Object Classification Using An Artificial Neural Network
Lee, D., Cheung, C., & Pritsker, D. (2019) · IEEE National Aerospace and Electronics Conference (NAECON) · doi:10.1109/naecon46414.2019.9058319 · Cited by 14
Modularized Morphing of Deep Convolutional Neural Networks: A Graph Approach
Wei, T., Wang, C., & Chen, C. W. (2020) · IEEE Transactions on Computers · doi:10.1109/tc.2020.2988006 · Cited by 34
TODQA: Efficient Task-Oriented Data Quality Assessment
Li, A., Zhang, L., Qian, J., Xiao, X., Li, X., & Xie, Y. (2019) · International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) · doi:10.1109/msn48538.2019.00028 · Cited by 15
A Hybrid Deep Learning Model for Protein–Protein Interactions Extraction from Biomedical Literature
Quan, C., Luo, Z., & Wang, S. (2020) · Applied Sciences · doi:10.3390/app10082690 · Cited by 14
3-Level Residual Capsule Network for Complex Datasets
Bhamidi, S. B. S., & El‐Sharkawy, M. (2020) · IEEE Latin American Symposium on Circuits and Systems (LASCAS) · doi:10.1109/lascas45839.2020.9068990 · Cited by 14
Bachhofner, S., Loghin, A.-M., Otepka, J., Pfeifer, N., Hornáček, M., Siposova, A., Schmidinger, N., Hornik, K., Schiller, N., Kähler, O., et al. (2020) · Remote Sensing · doi:10.3390/rs12081289 · Cited by 14
Aggressive Action Recognition Using 3D CNN Architectures
Saveliev, A. I., Uzdiaev, M., & Malov, D. (2019) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese.2019.00165 · Cited by 15
Multi-view feature learning for VHR remote sensing image classification
Guo, Y., Ji, J., Shi, D., Ye, Q., & Xie, H. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08713-z · Cited by 14
Layerwise Buffer Voltage Scaling for Energy-Efficient Convolutional Neural Network
Ha, M., Byun, Y., Moon, S., Lee, Y., & Lee, S. (2020) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2020.2992527 · Cited by 14
Using dual-channel CNN to classify hyperspectral image based on spatial-spectral information
Song, H.-F., Yang, W., Dai, S., Du, L., & Sun, Y. (2020) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2020195 · Cited by 14
Advances in the prediction of mouse liver microsomal studies: From machine learning to deep learning
Renn, A., Su, B., Hsin, L., Sun, J., & Tseng, Y. J. (2020) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1479 · Cited by 14
A convolutional fuzzy min-max neural network
Chavan, T. R., & Nandedkar, A. V. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.003 · Cited by 15
Image Hashing via Linear Discriminant Learning
Hong, W., Chang, Y.-T., Qin, H., Hung, W.-C., Tsai, Y., & Yang, M. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093487 · Cited by 14
Fang, J., & Liu, G. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2994744 · Cited by 14
Li, W., Xiang, S., Nie, W., Song, D., Liu, A.-A., Li, X., & Hao, T. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102275 · Cited by 14
Group Feedback Capsule Network
Ding, X., Wang, N., Gao, X., Li, J., Wang, X., & Liu, T. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2993931 · Cited by 13
Fuzzy Superpixels Based Semi-Supervised Similarity-Constrained CNN for PolSAR Image Classification
Guo, Y., Sun, Z., Qu, R., et al. (2020) · Remote Sensing · doi:10.3390/rs12101694 · Cited by 14
Explicit Semantic Decomposition for Definition Generation
Li, J., Bao, Y., Huang, S., Dai, X., & Chen, J. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.65 · Cited by 14
Defending Black Box Facial Recognition Classifiers Against Adversarial Attacks
Theagarajan, R., & Bhanu, B. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00414 · Cited by 14
Mining of multiple ailments correlated to diabetes mellitus
Reddy, S. S., Sethi, N., & Rajender, R. (2020) · Evolutionary Intelligence · doi:10.1007/s12065-020-00432-6 · Cited by 14
Barros, P. V. A., Barakova, E. I., & Wermter, S. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3002657 · Cited by 16
Fold Electrocardiogram Into a Fingerprint
Hsu, P., Hsu, P., & Liu, H. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00422 · Cited by 15
Joint Multi-view 2D Convolutional Neural Networks for 3D Object Classification
Xu, J., Zhang, X., Li, W., et al. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/443 · Cited by 14
Learning from noisy out-of-domain corpus using dataless classification
Jin, Y., Wanvarie, D., & Le, P. T. V. (2020) · Natural Language Engineering · doi:10.1017/s1351324920000340 · Cited by 12
A Throughput-Latency Co-Optimised Cascade of Convolutional Neural Network Classifiers
Kouris, A., Venieris, S. I., & Bouganis, C.-S. (2020) · Design, Automation, and Test in Europe · doi:10.23919/date48585.2020.9116248 · Cited by 15
Li, Y., Liu, C., Zhao, W. P., et al. (2020) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2020245 · Cited by 14
Kim, W., & Seok, J. (2020) · Scientific Reports · doi:10.1038/s41598-020-67545-x · Cited by 13
Deep Learning for Effective Refugee Tent Extraction Near Syria–Jordan Border
Lu, Y., Koperski, K., Kwan, C., & Li, J. (2020) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2020.2999354 · Cited by 16
Abbreviated View of Deepfake Videos Detection Techniques
Younus, M. A., & Hasan, T. M. (2020) · 2020 6th International Engineering Conference “Sustainable Technology and Development" (IEC) · doi:10.1109/iec49899.2020.9122916 · Cited by 17
Fully multi-target segmentation for breast ultrasound image based on fully convolutional network
Zhang, Y., Liu, Y., Cheng, H.-D., et al. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02200-1 · Cited by 14
Woo, J., Nguyen, T. V., Kim, J. H., Im, J., Im, S., Kim, Y., Min, K., & Moon, S. E. (2020) · Scientific Reports · doi:10.1038/s41598-020-68547-5 · Cited by 15
Color texture image retrieval based on Copula multivariate modeling in the Shearlet domain
Etemad, S., & Amirmazlaghani, M. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104256 · Cited by 14
Zhang, S., Wang, M., Xiong, J., Liu, S., & Chen, P. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3007399 · Cited by 14
PolygraphMR: Enhancing the Reliability and Dependability of CNNs
Latifi, S., Zamirai, B., & Mahlke, S. A. (2020) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn48063.2020.00029 · Cited by 14
Vehicle Counting using Deep Learning Models: A Comparative Study
Abdullah, A., & Oothariasamy, J. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110784 · Cited by 14
A CNN-based Path Trajectory Prediction Approach with Safety Constraints
Zaman, M., Zohrabi, N., & Abdelwahed, S. (2020) · IEEE Transportation Electrification Conference & Expo (ITEC) · doi:10.1109/itec48692.2020.9161731 · Cited by 14
Potential for Vertical Heterogeneity Prediction in Reservoir Basing on Machine Learning Methods
Song, H., Du, S., Wang, R., et al. (2020) · Geofluids · doi:10.1155/2020/3713525 · Cited by 14
Cloud-based efficient scheme for handwritten digit recognition
Shaukat, Z., Ali, S., Farooq, Q. U. A., Xiao, C., Sahiba, S., & Ditta, A. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09494-1 · Cited by 15
CSIDNet: Compact single image dehazing network for outdoor scene enhancement
Sharma, T., Agrawal, I., & Verma, N. K. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09496-z · Cited by 14
A semi-greedy neural network CAE-HL-CNN for SAR target recognition with limited training data
Qin, R., Fu, X., Dong, J., et al. (2020) · International Journal of Remote Sensing · doi:10.1080/01431161.2020.1766149 · Cited by 14
Saliency detection in human crowd images of different density levels using attention mechanism
Nguyen, M. T., Siritanawan, P., & Kotani, K. (2020) · Signal Processing Image Communication · doi:10.1016/j.image.2020.115976 · Cited by 14
Representation Learning from Limited Educational Data with Crowdsourced Labels
Wang, W., Xu, G., Ding, W., Huang, Y., Li, G., Tang, J., & Liu, Z. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3017122 · Cited by 12
Cellular State Transformations Using Deep Learning for Precision Medicine Applications
Targonski, C., Bender, M. R., Shealy, B. T., Husain, B., Paseman, B., Smith, M. C., & Feltus, F. A. (2020) · Patterns · doi:10.1016/j.patter.2020.100087 · Cited by 14
Kencana, C. W., Setiawan, E. B., & Kurniawan, I. (2020) · Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) · doi:10.29207/resti.v4i4.2038 · Cited by 14
DOMAIN ADAPTATION WITH CYCLEGAN FOR CHANGE DETECTION IN THE AMAZON FOREST
Vega, P. J. S., da Costa, G. A. O. P., Feitosa, R. Q., Happ, P. N., Ortega, M. X., Turnes, J. N., Almeida, C. A., & Heipke, C. (2020) · 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-xliii-b3-2020-1635-2020 · Cited by 14
Video scene parsing: An overview of deep learning methods and datasets
Yan, X., Gong, H., Jiang, Y., Xia, S., Zheng, F., You, X., & Shao, L. (2020) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2020.103077 · Cited by 14
HFNet: Forecasting Real-Time Electricity Price via Novel GRU Architectures
Yang, H., & Schell, K. R. (2020) · International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) · doi:10.1109/pmaps47429.2020.9183697 · Cited by 16
A Comparative Analysis of AlexNet and GoogLeNet with a Simple DCNN for Face Recognition
Kalaiarasi, P., & Rani, P. E. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-5029-4_54 · Cited by 19
Deep Face Rectification for 360° Dual-Fisheye Cameras
Li, Y.-H., Lo, I.-C., & Chen, H. H. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3019661 · Cited by 14
SketchOpt: Sketch-based Parametric Model Retrieval for Generative Design
Keshavarzi, M., Hutson, C., Cheng, C.-Y., et al. (2021) · Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3451620 · Cited by 14
Verifying Equivalence Properties of Neural Networks with ReLU Activation Functions
Büning, M. K., Kern, P., & Sinz, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58475-7_50 · Cited by 14
Energy Adaptive Convolution Neural Network Using Dynamic Partial Reconfiguration
Youssef, E., Elsemary, H. A., El-Moursy, M. A., et al. (2020) · Conference proceedings · doi:10.1109/mwscas48704.2020.9184640 · Cited by 14
Efficient and Accurate 3D Finger Knuckle Matching Using Surface Key Points
Cheng, K. H. M., & Kumar, A. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3021294 · Cited by 14
Pancreas segmentation based on an adversarial model under two-tier constraints
Li, M., Lian, F., & Guo, S. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/abb6bf · Cited by 14
A discrete cosine transform-based query efficient attack on black-box object detectors
Kuang, X., Gao, X., Wang, L., Zhao, G., Ke, L., & Zhang, Q. (2020) · Information Sciences · doi:10.1016/j.ins.2020.05.089 · Cited by 14
Fusion of Deep Learning and Image Processing Techniques for Breast Cancer Diagnosis
Devi, V. A., & Nayyar, A. (2020) · Studies in computational intelligence · doi:10.1007/978-981-15-6321-8_1 · Cited by 14
Tackling Algorithmic Bias in Neural-Network Classifiers using Wasserstein-2 Regularization
Risser, L., González-Sanz, A., Vincenot, Q., & Loubes, J. (2022) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-022-01090-2 · Cited by 23
Kilimci, Z. H., Hasan, Y., & Akyokuş, S. (2020) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista49547.2020.9194624 · Cited by 14
Towards Mapping Images to Text Using Deep-Learning Architectures
Onita, D., Birlutiu, A., & Dinu, L. P. (2020) · Mathematics · doi:10.3390/math8091606 · Cited by 14
Chen, L., Shi, W., Fan, C., et al. (2020) · Remote Sensing · doi:10.3390/rs12193115 · Cited by 14
Yang, J.-Y., Chen, U.-K., Chang, K.-C., et al. (2020) · International Conference on Advanced Robotics and Intelligent Systems (ARIS) · doi:10.1109/aris50834.2020.9205791 · Cited by 14
Computer Vision-Assisted Instant Alerts in 5G
Tseng, Y.-Y., Hsu, P.-M., Chen, J., & Tseng, Y. (2020) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn49398.2020.9209751 · Cited by 14
Detecting Underwater Sea Litter Using Deep Neural Networks: An Initial Study
Musić, J., Kružić, S., Stančić, I., et al. (2020) · International Conference on Smart and Sustainable Technologies (SpliTech) · doi:10.23919/splitech49282.2020.9243709 · Cited by 14
TRADER: trace divergence analysis and embedding regulation for debugging recurrent neural networks
Tao, G., Ma, S., Liu, Y., Xu, Q., & Zhang, X. (2020) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3377811.3380423 · Cited by 15
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
