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 33,901–34,000 of 108,435
Efficient Evaluation of Image Quality via Deep-Learning Approximation of Perceptual Metrics
Artusi, A., Banterle, F., Carra, F., & Moreo, A. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2944079 · Cited by 23
CLPNet: Cleft Lip and Palate Surgery Support With Deep Learning
Li, Y., Cheng, J., Mei, H., et al. (2019) · doi:10.1109/embc.2019.8857799 · Cited by 23
Radiomics-guided GAN for Segmentation of Liver Tumor Without Contrast Agents
Xiao, X., Zhao, J., Qiang, Y., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32245-8_27 · Cited by 23
A Parallel Neural Network Approach for Faster Rumor Identification in Online Social Networks
Srinivasan, S., & Babu, L. D. D. (2019) · International Journal on Semantic Web and Information Systems · doi:10.4018/ijswis.2019100105 · Cited by 23
Semantic Image Segmentation for Autonomous Driving Using Fully Convolutional Networks
Kaymak, Ç., & Uçar, A. (2019) · International Artificial Intelligence and Data Processing Symposium (IDAP) · doi:10.1109/idap.2019.8875923 · Cited by 24
Deep Active Shape Model for Robust Object Fitting
Medley, D. O., Santiago, C., & Nascimento, J. C. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2948728 · Cited by 23
Prilianti, K. R., Brotosudarmo, T. H. P., Anam, S., & Suryanto, A. (2019) · AIP conference proceedings · doi:10.1063/1.5094284 · Cited by 28
Vehicle global 6-DoF pose estimation under traffic surveillance camera
Zhang, S., Wang, C., He, Z., Li, Q., Lin, X., Li, X. S., Zhang, J., Yang, C., & Li, J. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.11.005 · Cited by 22
Deep Grammatical Multi-classifier for Continuous Sign Language Recognition
Wei, C., Zhou, W., Pu, J., & Li, H. (2019) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm.2019.00027 · Cited by 25
Temporal Accumulative Features for Sign Language Recognition
Kındıroğlu, A. A., Özdemir, O., & Akarun, L. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00164 · Cited by 23
Lung Cancer Detection from Computed Tomography (CT) Scans using Convolutional Neural Network
Khumancha, M. B., Barai, A., & Rao, C. B. R. (2019) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt45670.2019.8944824 · Cited by 26
Chen, Y., Tang, L., Yang, X., Bilal, M., & Li, Q. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.12.098 · Cited by 23
Dialog State Tracking with Reinforced Data Augmentation
Yin, Y., Shang, L., Jiang, X., Chen, X. D., & Liu, Q. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6491 · Cited by 26
Joint low rank embedded multiple features learning for audio–visual emotion recognition
Wang, Z., Wang, L., & Huang, H. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.01.017 · Cited by 23
Suzuki, M., SASAKI, M., Kamata, K., Nakayama, A., Shibamoto, I., & Tamada, Y. (2020) · Advanced Biomedical Engineering · doi:10.14326/abe.9.10 · Cited by 23
A Review on Semantic Segmentation from a Modern Perspective
Atif, N., Bhuyan, M. K., & Ahamed, S. (2019) · IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · doi:10.1109/upcon47278.2019.8980189 · Cited by 28
Stochastic Mirror Descent on Overparameterized Nonlinear Models
Azizan, N., Lale, S., & Hassibi, B. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3087480 · Cited by 66
Deep learning–based automatic bird identification system for offshore wind farms
Niemi, J., & Tanttu, J. T. (2020) · Wind Energy · doi:10.1002/we.2492 · Cited by 26
An Efficient Accelerator for Multiple Convolutions From the Sparsity Perspective
Chen, Q., Huang, Y., Sun, R., Song, W., Lu, Z., Fu, Y., & Li, L. (2020) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2020.2976454 · Cited by 22
Selective auditory attention detection based on effective connectivity by single-trial EEG
Geravanchizadeh, M., & Gavgani, S. B. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab7c8d · Cited by 23
Xiang, Z., Sang, J., Zhang, Q., Cai, B., Xia, X., & Wu, W. (2020) · IEEE Access · doi:10.1109/access.2020.2978110 · Cited by 24
Mode decomposition based deep learning model for multi-section traffic prediction
Pholsena, K., Pan, L., & Zheng, Z. (2020) · World Wide Web · doi:10.1007/s11280-020-00791-1 · Cited by 23
Ensemble Residual Networks for Short-Term Load Forecasting
Xu, Q., Yang, X., & Xin, H. (2020) · IEEE Access · doi:10.1109/access.2020.2984722 · Cited by 27
Kang, H. G., Park, J., Cho, K., & Kang, D. (2020) · Applied Sciences · doi:10.3390/app10072628 · Cited by 23
Scene Recognition from Image Using Convolutional Neural Network
Masood, S., Ahsan, U., Munawwar, F., Rizvi, D. R., & Ahmed, M. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.03.400 · Cited by 27
Convolutional Neural Network Based Skin Lesion Analysis for Classifying Melanoma
Guha, S. R., & Haque, S. (2019) · International Conference on Sustainable Technologies for Industry 5.0 (STI) · doi:10.1109/sti47673.2019.9067979 · Cited by 23
Deep embedding of concept ontology for hierarchical fashion recognition
Kuang, Z., Zhang, X., Yu, J., Li, Z., & Jianping Fan, B. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.085 · Cited by 22
Multiple player tracking in basketball court videos
Fu, X., Zhang, K., Wang, C., & Fan, C. (2020) · Journal of Real-Time Image Processing · doi:10.1007/s11554-020-00968-x · Cited by 24
Class Balanced Loss for Image Classification
Wang, L., Wang, C., Sun, Z., Cheng, S., & Guo, L. (2020) · IEEE Access · doi:10.1109/access.2020.2991237 · Cited by 28
An Accurate Device-Free Action Recognition System Using Two-Stream Network
Sheng, B., Fang, Y., Xiao, F., & Sun, L. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.2993901 · Cited by 22
Reviewing inference performance of state-of-the-art deep learning frameworks
Ülker, B., Stuijk, S., Corporaal, H., & Wijnhoven, R. G. J. (2020) · International Workshop on Software and Compilers for Embedded Systems (SCOPES) · doi:10.1145/3378678.3391882 · Cited by 22
Neural network with multiple connection weights
Zhang, J., Hu, J., & Liu, J. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107481 · Cited by 26
A Novel Neural Network Method for Face Recognition With a Single Sample Per Person
Abdelmaksoud, M., Nabil, E., Farag, I., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2999030 · Cited by 23
Lo, C., Wu, Y., Li, Y., & Lee, C.-C. (2020) · Applied Sciences · doi:10.3390/app10124059 · Cited by 24
Hierarchical Instance Feature Alignment for 2D Image-Based 3D Shape Retrieval
Zhou, H., Nie, W., Li, W., et al. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/117 · Cited by 23
Real-time multimodal ADL recognition using convolution neural networks
Madhuranga, D., Madushan, R., Siriwardane, C., & Gunasekera, K. (2020) · The Visual Computer · doi:10.1007/s00371-020-01864-y · Cited by 23
Learning to Structure an Image With Few Colors
Hou, Y., Zheng, L., & Gould, S. J. (2020) · 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/cvpr42600.2020.01013 · Cited by 25
Low-Latency Privacy-Preserving Outsourcing of Deep Neural Network Inference
Tian, Y., Njilla, L., Yuan, J., & Yu, S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3003468 · Cited by 23
Achieving Better Kinship Recognition Through Better Baseline
Shadrikov, A. (2020) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg47880.2020.00137 · Cited by 27
Romaszkan, W., Li, T., Melton, T., Pamarti, S., & Gupta, P. (2020) · Design, Automation, and Test in Europe · doi:10.23919/date48585.2020.9116289 · Cited by 24
A Real-time Robust Approach for Tracking UAVs in Infrared Videos
Wu, H., Li, W., Li, W., & Liu, G. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00524 · Cited by 25
da Silva, G. L. F., Diniz, P. S., Ferreira, J. L., França, J. V. F., Silva, A. C., de Paiva, A. C., & de Cavalcanti, E. A. A. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02199-5 · Cited by 23
ESSA: An energy-Aware bit-Serial streaming deep convolutional neural network accelerator
Hsu, L.-C., Chiu, C., Lin, K.-T., Chou, H.-H., & Pu, Y.-Y. (2020) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2020.101831 · Cited by 24
Computational intelligence for modeling of asphalt pavement surface distress
Ranjbar, S., Nejad, F. M., Zakeri, H., & Gandomi, A. H. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-818961-0.00003-x · Cited by 26
Object Localization and Depth Estimation for Eye-in-Hand Manipulator Using Mono Camera
Muslikhin, M., Horng, J.-R., Yang, S.-Y., & Wang, M. (2020) · IEEE Access · doi:10.1109/access.2020.3006843 · Cited by 23
Learning Accurate and Human-Like Driving using Semantic Maps and Attention
Hecker, S., Dai, D., Liniger, A., Hahner, M., & Gool, L. V. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341157 · Cited by 27
MARBLE: A Multi-GPU Aware Job Scheduler for Deep Learning on HPC Systems
Han, J., Rafique, M. M., Xu, L., Butt, A. R., Lim, S., & Vazhkudai, S. S. (2020) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid49817.2020.00-66 · Cited by 27
Zhu, L. (2020) · IEEE Access · doi:10.1109/access.2020.3010969 · Cited by 23
Attack Selectivity of Adversarial Examples in Remote Sensing Image Scene Classification
Chen, L., Li, H., Zhu, G., Li, Q., Zhu, J., Huang, H., Peng, J., & Zhao, L. (2020) · IEEE Access · doi:10.1109/access.2020.3011639 · Cited by 24
A Novel Fusion of Deep Learning and Android Application for Real-Time Mango Fruits Disease Detection
Ashok, V., & Vinod, D. S. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-5400-1_74 · Cited by 23
A Method of DC Arc Detection in All-Electric Aircraft
Li, T., Jiao, Z., Wang, L., et al. (2020) · Energies · doi:10.3390/en13164190 · Cited by 23
Soltan, S., Oleinikov, A., Demirci, M. F., & Shintemirov, A. (2020) · Robotics · doi:10.3390/robotics9030063 · Cited by 24
Multi-source domain adaptation with graph embedding and adaptive label prediction
Ma, A., You, F., Jing, M., Li, J., & Lü, K. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102367 · Cited by 23
Alsaih, K., Yusoff, M. Z., Faye, I., Tang, T. B., & Mériaudeau, F. (2020) · IEEE Access · doi:10.1109/access.2020.3017449 · Cited by 23
Dual subspace discriminative projection learning
Belous, G., Busch, A. W., & Gao, Y. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107581 · Cited by 22
Wibisono, A., Wisesa, H. A., Rahmadhani, Z. P., et al. (2020) · Journal Of Big Data · doi:10.1186/s40537-020-00342-5 · Cited by 23
Robust Hand Shape Features for Dynamic Hand Gesture Recognition Using Multi-Level Feature LSTM
Do, N.-T., Kim, S.-H., Yang, H.-J., & Lee, G.-S. (2020) · Applied Sciences · doi:10.3390/app10186293 · Cited by 25
Distributed Cooperative Deep Transfer Learning for Industrial Image Recognition
Maschler, B., Kamm, S., Jazdi, N., & Weyrich, M. (2020) · Procedia CIRP · doi:10.1016/j.procir.2020.03.056 · Cited by 25
Özbulak, U., Vandersmissen, B., Jalalvand, A., Couckuyt, I., Messem, A. V., & Neve, W. D. (2020) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2020.103111 · Cited by 27
Ensemble Learning Using Bagging And Inception-V3 For Anomaly Detection In Surveillance Videos
Zahid, Y., Tahir, M. A., & Durrani, M. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9190673 · Cited by 22
Pedestrian as Points: An Improved Anchor-Free Method for Center-Based Pedestrian Detection
Cai, J., Lee, F., Yang, S., Lin, C., Chen, H., Kotani, K., & Chen, Q. (2020) · IEEE Access · doi:10.1109/access.2020.3027590 · Cited by 22
Hierarchical classification of fine-art paintings using deep neural networks
Mohammadi, M. R., & Rustaee, F. (2020) · Iran Journal of Computer Science · doi:10.1007/s42044-020-00072-0 · Cited by 23
Energy-Efficient Image Recognition System for Marine Life
Demir, H. S., Christen, J. B., & Ozev, S. (2020) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2020.3012745 · Cited by 28
Kojima, K., Tadaka, S., Katsuoka, F., Tamiya, G., Yamamoto, M. P., & Kinoshita, K. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008207 · Cited by 24
AVOT: Audio-Visual Object Tracking of Multiple Objects for Robotics
Wilson, J., & Lin, M. C. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197528 · Cited by 24
Global optimization of objective functions represented by ReLU networks
Strong, C., Wu, H.-Z., Zelji'c, A., Julian, K. D., Katz, G., Barrett, C. W., & Kochenderfer, M. J. (2020) · Machine-mediated learning · doi:10.1007/s10994-021-06050-2 · Cited by 40
Residual Dual Scale Scene Text Spotting by Fusing Bottom-Up and Top-Down Processing
Feng, W., Yin, F., Zhang, X.-Y., He, W., & Liu, C. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01388-x · Cited by 23
Reweighting and information-guidance networks for Few-Shot Learning
Ji, Z., Chai, X., Yu, Y., & Zhang, Z. M. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.07.128 · Cited by 23
High Performance and Portable Convolution Operators for Multicore Processors
Juan, P. S., Castelló, A., Dolz, M. F., Alonso, P., & Quintana–Ort́ı, E. S. (2020) · International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) · doi:10.1109/sbac-pad49847.2020.00023 · Cited by 23
PRDNet: Medical image segmentation based on parallel residual and dilated network
Guo, H., & Yang, D. (2020) · Measurement · doi:10.1016/j.measurement.2020.108661 · Cited by 23
Power of Attention in MOOC Dropout Prediction
Yin, S., Lei, L., Wang, H., & Chen, W. (2020) · IEEE Access · doi:10.1109/access.2020.3035687 · Cited by 24
Calibration-Aided Edge Inference Offloading via Adaptive Model Partitioning of Deep Neural Networks
Pacheco, R. G., Couto, R. S., & Simeone, O. (2021) · International Conference on Communications · doi:10.1109/icc42927.2021.9500760 · Cited by 23
Learning with continuous piecewise linear decision trees
Tao, Q., Zhen, L., Xu, J., Xie, N., Wang, S., & Suykens, J. A. K. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114214 · Cited by 24
Deep Learning Based Motion Tracking of Ultrasound Image Sequences
Bharadwaj, S. S., & Almekkawy, M. K. (2020) · IEEE International Ultrasonics Symposium · doi:10.1109/ius46767.2020.9251739 · Cited by 24
Yu, W., Liu, G., Zhu, L. X., & Yu, W. (2020) · Solar Energy · doi:10.1016/j.solener.2020.09.026 · Cited by 26
A-HRNet: Attention Based High Resolution Network for Human pose estimation
Li, Y., Wang, C., Cao, Y., Liu, B., Luo, Y., & Zhang, H. (2020) · International Conference on Transdisciplinary AI (TransAI) · doi:10.1109/transai49837.2020.00016 · Cited by 28
Using SURF to Improve ResNet-50 Model for Poultry Disease Recognition Algorithm
Quach, L.-D., Quoc, N. P., Thi, N. H., et al. (2020) · doi:10.1109/icci51257.2020.9247698 · Cited by 23
Kallioras, N. A., & Lagaros, N. D. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05480-8 · Cited by 23
HYDRA: Hybrid Deep Magnetic Resonance Fingerprinting
Song, P., Eldar, Y., Mazor, G., & Rodrigues, M. R. D. (2019) · UCL Discovery (University College London) · Cited by 22
Sarabu, A., & Santra, A. K. (2020) · Data · doi:10.3390/data5040104 · Cited by 24
Speculative Backpropagation for CNN Parallel Training
Park, S., & Suh, T. (2020) · IEEE Access · doi:10.1109/access.2020.3040849 · Cited by 23
Convolutional Support Vector Models: Prediction of Coronavirus Disease Using Chest X-rays
Maia, M., Pimentel, J. S., Pereira, I. S., Gondim, J. A. M., Barreto, M. E., & Ara, A. (2020) · Information · doi:10.3390/info11120548 · Cited by 26
Fabric defect detection using deep learning: An Improved Faster R-approach
An, M., Wang, S., Zheng, L., et al. (2020) · International Conference on Computer Vision, Image and Deep Learning (CVIDL) · doi:10.1109/cvidl51233.2020.00-78 · Cited by 23
Cognitive Evaluation of Machine Learning Agents
Kadam, S., & Vaidya, V. G. (2020) · Cognitive Systems Research · doi:10.1016/j.cogsys.2020.11.003 · Cited by 24
Viewpoint robust knowledge distillation for accelerating vehicle re-identification
Xie, Y. F., Shen, F., Zhu, J., et al. (2021) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-021-00767-x · Cited by 23
Kurek, J., Antoniuk, I., Świderski, B., et al. (2020) · Sensors · doi:10.3390/s20236978 · Cited by 23
Xia, Z., Chen, J., Huang, Q., Luo, J., & Hu, J. (2020) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2020.3039346 · Cited by 24
Adversarial Analysis for Source Camera Identification
Wang, B., Zhao, M., Wang, W., Dai, X., Li, Y., & Guo, Y. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.3047084 · Cited by 23
Normal and Abnormal Heart Rates Recognition Using Transfer Learning
Alafif, T., Boulares, M., Barnawi, A., Alafif, T., Althobaiti, H., & Alferaidi, A. (2020) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse50997.2020.9287514 · Cited by 26
Practical fluorescence reconstruction microscopy for large samples and low-magnification imaging
LaChance, J., & Cohen, D. J. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008443 · Cited by 23
Machine Learning Based Plant Diseases Detection: A Review
Pardede, H. F., Suryawati, E., Krisnandi, D., Yuwana, R. S., & Zilvan, V. (2020) · International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET) · doi:10.1109/icramet51080.2020.9298619 · Cited by 28
Fusion of region based extracted features for instance- and class-based CBIR applications
Pradhan, J., Pal, A. K., Banka, H., & Dansena, P. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.107063 · Cited by 24
Additive deep feature optimization for semantic image retrieval
Hussain, S., Zia, M. A., & Arshad, W. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114545 · Cited by 24
Improving deep hyperspectral image classification performance with spectral unmixing
Guo, A. J. X., & Zhu, F. (2021) · Signal Processing · doi:10.1016/j.sigpro.2020.107949 · Cited by 23
Artificial Vision by Deep CNN Neocognitron
Fukushima, K. (2021) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2020.3042785 · Cited by 27
Hu, J. S., Rao, L., Zhu, Y., Zhang, G., & Yu, D. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00735 · Cited by 22
AdversarialQR: An adversarial patch in QR code format
Chindaudom, A., Siritanawan, P., Sumongkayothin, K., & Kotani, K. (2020) · International Conference on Informatics, Electronics & Vision (ICIEV) · doi:10.1109/icievicivpr48672.2020.9306675 · Cited by 26
Shi, Y. B., Ma, D., Lv, J., & Li, J. (2021) · IEEE Access · doi:10.1109/access.2021.3051015 · Cited by 23
Ahlswede, S., Asam, S., & Röder, A. (2021) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.15.018501 · Cited by 24
Event Popularity Prediction Using Influential Hashtags From Social Media
Chen, X., Zhou, X., Chan, J., Chen, L., Sellis, T., & Zhang, Y. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3048428 · Cited by 23
Metadata
- Authors
- Krizhevsky, A., Sutskever, I., & Hinton, G. E.
- Affiliations
- Google (United States) (US)
- University of Toronto (CA)
- Venue
- Communications of the ACM
- Published
- 2017-05-24
- Volume
- 60
- Issue
- 6
- Pages
- 84-90
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2163605009
- Cited by
- 112,155
- References
- 27
- Citations by year
- 2026: 2710 · 2025: 7710 · 2024: 10211 · 2023: 12267 · 2022: 12280 · 2021: 15497 · 2020: 14538 · 2019: 13976 · 2018: 9999 · 2017: 6661
- FWCI
- 5127.18
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- neural information processing systems
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- ImageNet, image classification, deep convolutional neural networks, dropout regularization, overfitting
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-11, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-11 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
