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 34,901–35,000 of 108,435
Camera pose estimation in multi-view environments: From virtual scenarios to the real world
Charco, J. L., Sappa, A. D., Vintimilla, B. X., & Velesaca, H. O. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104182 · Cited by 21
Seum, A., Raj, A. H., Sakib, S., & Hossain, T. (2020) · International Conference on Electrical and Computer Engineering (ICECE) · doi:10.1109/icece51571.2020.9393129 · Cited by 22
Multi-agent reinforcement learning approach for hedging portfolio problem
Pham, U. H., Luu, Q., & Tran, H. D. (2021) · Soft Computing · doi:10.1007/s00500-021-05801-6 · Cited by 29
Refined Image Segmentation for Calorie Estimation of Multiple-dish food items
Poply, P., & Jothi, J. A. A. (2021) · International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) · doi:10.1109/icccis51004.2021.9397169 · Cited by 23
Blind identification of convolutional codes based on deep learning
Wang, J., Tang, C., Huang, H., Wang, H., & Li, J. (2021) · Digital Signal Processing · doi:10.1016/j.dsp.2021.103086 · Cited by 26
Determining Chess Game State from an Image
Wölflein, G., & Arandjelović, O. (2021) · Journal of Imaging · doi:10.3390/jimaging7060094 · Cited by 36
A Double Channel CNN-LSTM Model for Text Classification
Liang, S., Zhu, B. B., Yuying, Z., et al. (2020) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-smartcity-dss50907.2020.00169 · Cited by 22
de Lima, J. M. M., & de Araújo, F. M. U. (2021) · Sensors · doi:10.3390/s21103430 · Cited by 22
End-to-End Deep One-Class Learning for Anomaly Detection in UAV Video Stream
Hamdi, S., Bouindour, S., Snoussi, H., Wang, T., & Abid, M. (2021) · Journal of Imaging · doi:10.3390/jimaging7050090 · Cited by 26
Wang, L., Zhou, Y., Hu, Q., et al. (2021) · Remote Sensing · doi:10.3390/rs13101975 · Cited by 22
Deep learning of material transport in complex neurite networks
Li, A., Farimani, A. B., & Zhang, Y. J. (2021) · Scientific Reports · doi:10.1038/s41598-021-90724-3 · Cited by 25
A Time Series Data Augmentation Method Based on Dynamic Time Warping
Yang, X., Zhang, Z., Cui, X., & Cui, R. (2021) · International Conference on Computer Communication and Artificial Intelligence (CCAI) · doi:10.1109/ccai50917.2021.9447507 · Cited by 23
A Literature Review on Generative Adversarial Networks with Its Applications in Healthcare
Saaran, V., Kushwaha, V., Gupta, S., & Agarwal, G. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-33-6981-8_18 · Cited by 21
LOSP: Overlap Synchronization Parallel With Local Compensation for Fast Distributed Training
Wang, H., Qu, Z., Guo, S., et al. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3087272 · Cited by 22
Probing transfer learning with a model of synthetic correlated datasets
Gerace, F., Saglietti, L., Mannelli, S. S., Saxe, A., & Zdeborová, L. (2022) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ac4f3f · Cited by 25
Leveraging Domain Adaptation as a Defense Against Membership Inference Attacks
Huang, H., Luo, W., Zeng, G., Weng, J., Zhang, Y., & Yang, A. (2021) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2021.3088480 · Cited by 30
Li, Y., Chen, C., Chen, F., Chen, C., Gao, R., Yang, B., Si, R., & Lv, X. (2021) · Photodiagnosis and Photodynamic Therapy · doi:10.1016/j.pdpdt.2021.102382 · Cited by 25
Activity recognition of FMCW radar human signatures using tower convolutional neural networks
Victoria, A. H., & Maragatham, G. (2021) · Wireless Networks · doi:10.1007/s11276-021-02670-7 · Cited by 22
Learning from the Best: Rationalizing Predictions by Adversarial Information Calibration
Sha, L., Camburu, O.-M., & Lukasiewicz, T. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17623 · Cited by 21
Towards Reading Beyond Faces for Sparsity-aware 3D/4D Affect Recognition
Behzad, M., Vo, N., Li, X., & Zhao, G. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.06.023 · Cited by 21
Edge Guided Progressively Generative Image Outpainting
Lin, H., Pagnucco, M., & Song, Y. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00090 · Cited by 22
Through-Wall Human Activity Classification Using Complex-Valued Convolutional Neural Network
Wang, X., Chen, P., Xie, H., et al. (2021) · Proceedings of the IEEE National Radar Conference · doi:10.1109/radarconf2147009.2021.9455169 · Cited by 22
Self-supervised Motion Learning from Static Images
Huang, Z., Zhang, S., Jiang, J., Tang, M., Jin, R., & Ang, M. H. (2021) · 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/cvpr46437.2021.00133 · Cited by 29
Neural architecture search of echocardiography view classifiers
Azarmehr, N., Zolgharni, M., Ye, X., Howard, J. P., Lane, E. S., Labs, R. B., Shun-Shin, M. J., Cole, G., Bidaut, L. M., & Francis, D. P. (2021) · Journal of medical imaging · doi:10.1117/1.jmi.8.3.034002 · Cited by 24
Zhang, J., Meng, Y., & Chen, Z. (2021) · IEEE Access · doi:10.1109/access.2021.3095405 · Cited by 22
Energy-Efficient and Interference-Aware VNF Placement with Deep Reinforcement Learning
Mu, Y., Wang, L., & Zhao, J. (2021) · IFIP Networking Conference (IFIP Networking) · doi:10.23919/ifipnetworking52078.2021.9472805 · Cited by 22
Ortega, R. F., Irurita, J., Campo, E. J. E., & Mesejo, P. (2021) · International Journal of Legal Medicine · doi:10.1007/s00414-021-02660-6 · Cited by 24
HeSA: Heterogeneous Systolic Array Architecture for Compact CNNs Hardware Accelerators
Xu, R., Ma, S., Wang, Y., & Guo, Y. (2021) · Design, Automation, and Test in Europe · doi:10.23919/date51398.2021.9474145 · Cited by 22
Hyperspectral Image Classification Using Deep Learning Models: A Review
Kumar, D., & Kumar, D. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1950/1/012087 · Cited by 25
A systematic evaluation of the computational tools for lncRNA identification
Zheng, H., Talukder, A., Li, X., & Hu, H. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab285 · Cited by 25
Guided Spatial Transformers for Facial Expression Recognition
Luna-Jiménez, C., Cristóbal-Martín, J., Kleinlein, R., Gil-Martín, M., Moya, J. M., & Fernández-Martínez, F. (2021) · Applied Sciences · doi:10.3390/app11167217 · Cited by 22
Improved Object Detection Algorithm of YOLOv3 Remote Sensing Image
Wu, K., Bai, C., Wang, D., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3103522 · Cited by 22
Shah, S., Mishra, R., Szczurowska, A., & Guziński, M. (2021) · Polish Journal of Radiology · doi:10.5114/pjr.2021.108257 · Cited by 22
He, X., Wang, Y., Wang, X., Huang, W., Zhao, S., & Chen, X. (2021) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2021.100955 · Cited by 23
de Lima, R. P., & Duarte, D. (2021) · Geosciences · doi:10.3390/geosciences11080336 · Cited by 25
An Enhanced Insect Pest Counter Based on Saliency Map and Improved Non-Maximum Suppression
Guo, Q., Wang, C., Xiao, D., & Huang, Q. (2021) · Insects · doi:10.3390/insects12080705 · Cited by 22
Ikeno, K., Fukuda, T., & Yabuki, N. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2021.101380 · Cited by 22
Zhang, Y., Deng, M., He, F., et al. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3103429 · Cited by 22
A Comprehensive Evaluation and Benchmarking of Convolutional Neural Networks for Melanoma Diagnosis
Alzahrani, S., Al‐Bander, B., & Al‐Nuaimy, W. (2021) · Cancers · doi:10.3390/cancers13174494 · Cited by 23
Exposing DeepFake Videos Using Attention Based Convolutional LSTM Network
Su, Y., Xia, H., Liang, Q., & Nie, W. (2021) · Neural Processing Letters · doi:10.1007/s11063-021-10588-6 · Cited by 24
Enhancing Time Series Predictors with Generalized Extreme Value Loss
Zhang, M., Ding, D., Pan, X., & Yang, M. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3108831 · Cited by 24
Basiri, S., Taheri, A., Meghdari, A. F., Boroushaki, M., & Alemi, M. (2021) · International Journal of Social Robotics · doi:10.1007/s12369-021-00819-0 · Cited by 26
Traffic Light and Arrow Signal Recognition Based on a Unified Network
Yeh, T.-W., Lin, H., & Chang, C.-C. (2021) · Applied Sciences · doi:10.3390/app11178066 · Cited by 23
Morphence: Moving Target Defense Against Adversarial Examples
Amich, A., & Eshete, B. (2021) · Annual Computer Security Applications Conference · doi:10.1145/3485832.3485899 · Cited by 24
A Smart Adversarial Attack on Deep Hashing Based Image Retrieval
Lu, J., Chen, M., Sun, Y., Wang, W., Wang, Y., & Yang, X. (2021) · International Conference on Multimedia Retrieval · doi:10.1145/3460426.3463640 · Cited by 22
CSHE: network pruning by using cluster similarity and matrix eigenvalues
Shao, M., Dai, J., Wang, R., Kuang, J., & Zuo, W. (2021) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-021-01411-8 · Cited by 21
Li, D., Yu, W., Wang, K., et al. (2021) · Signal Processing Image Communication · doi:10.1016/j.image.2021.116500 · Cited by 22
Crowd Detection and Analysis for Surveillance Videos using Deep Learning
Ahmed, A., Bansal, P., Khan, A., et al. (2021) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc51422.2021.9532683 · Cited by 22
DFTSA-Net: Deep Feature Transfer-Based Stacked Autoencoder Network for DME Diagnosis
Atteia, G. E., Samee, N. M. A., & Hassan, H. Z. (2021) · Entropy · doi:10.3390/e23101251 · Cited by 22
Env-QA: A Video Question Answering Benchmark for Comprehensive Understanding of Dynamic Environments
Gao, D., Wang, R., Bai, Z., & Chen, X. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00170 · Cited by 26
MESH-BASED GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR MODELING MATERIALS WITH MICROSTRUCTURE
Frankel, A., Safta, C., Alleman, C. N., & Jones, R. E. (2021) · Journal of Machine Learning for Modeling and Computing · doi:10.1615/jmachlearnmodelcomput.2021039688 · Cited by 21
The Coming of Age of Interpretable and Explainable Machine Learning Models
Lisboa, P. J. G., Saralajew, S., Vellido, A., & Villmann, T. (2021) · European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) · doi:10.14428/esann/2021.es2021-2 · Cited by 23
Trajectory Optimization for Manipulation of Deformable Objects: Assembly of Belt Drive Units
Jin, S., Romeres, D., Ragunathan, A., Jha, D. K., & Tomizuka, M. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561556 · Cited by 23
Pereira, P. M. M., Thomaz, L. A., Tavora, L. M. N., Assunção, P., Fonseca-Pinto, R., Paiva, R. P., & de Faria, S. M. M. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102254 · Cited by 23
Modelling trend life cycles in scientific research using the Logistic and Gompertz equations
Tattershall, E., Nenadić, G., & Stevens, R. (2021) · Scientometrics · doi:10.1007/s11192-021-04137-0 · Cited by 32
A New Convolutional Kernel Classifier for Hyperspectral Image Classification
Ansari, M., Homayouni, S., Safari, A., et al. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3123087 · Cited by 21
A large-scale hyperspectral dataset for flower classification
Zheng, Y., Zhang, T., & Fu, Y. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107647 · Cited by 22
TFE: A Transformer Architecture for Occlusion Aware Facial Expression Recognition
Gao, J., & Zhao, Y. (2021) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2021.763100 · Cited by 24
Residual Quantization for Low Bit-Width Neural Networks
Li, Z., Ni, B., Yang, X., Zhang, W., & Gao, W. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3124095 · Cited by 23
Software Defect Prediction Harnessing on Multi 1-Dimensional Convolutional Neural Network Structure
Pin, K., Chang, J. H., & Nam, Y. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.022085 · Cited by 24
Neural-PIM: Efficient Processing-In-Memory with Neural Approximation of Peripherals
Cao, W., Zhao, Y., Boloor, A., Han, Y., Zhang, X., & Jiang, L. (2021) · IEEE Transactions on Computers · doi:10.1109/tc.2021.3122905 · Cited by 23
CNN and LSTM based Encoder-Decoder for Anomaly Detection in Multivariate Time Series
Zhang, A., Zhao, X., & Wang, L. (2021) · IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · doi:10.1109/itnec52019.2021.9587207 · Cited by 30
Yang, K., Yang, T., Yao, Y., et al. (2021) · Ocean & Coastal Management · doi:10.1016/j.ocecoaman.2021.105971 · Cited by 22
Optical Braille Recognition Using Object Detection Neural Network
Ovodov, I. G. (2021) · IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw54120.2021.00200 · Cited by 22
Deep Transfer Learning Approach for Identifying Slope Surface Cracks
Yang, Y., & Mei, G. (2021) · Applied Sciences · doi:10.3390/app112311193 · Cited by 22
Traffic Sign Recognition Algorithm Based on Improved YOLOv5s
Liu, X., Jiang, X., Hu, H., Ding, R., Li, H., & Da, C. (2021) · International Conference on Control, Automation and Information Sciences (ICCAIS) · doi:10.1109/iccais52680.2021.9624657 · Cited by 23
Denoising and fuel spray droplet detection from light-scattered images using deep learning
Hasti, V. R., & Shin, D. (2021) · Energy and AI · doi:10.1016/j.egyai.2021.100130 · Cited by 22
Kuck, T. N., Filho, P. F. F. S., Sano, E. E., Bispo, P. D. C., Shiguemori, E. H., & Dalagnol, R. (2021) · Remote Sensing · doi:10.3390/rs13234944 · Cited by 23
Nian, R., Cai, Y., Zhang, Z., He, H., Wu, J., Yuan, Q., Geng, X., Qian, Y., Yang, H., & He, B. (2021) · Frontiers in Marine Science · doi:10.3389/fmars.2021.753942 · Cited by 23
ROBY: Evaluating the adversarial robustness of a deep model by its decision boundaries
Jin, H., Chen, J., Zheng, H., Wang, Z., Xiao, J., Yu, S., & Ming, Z. (2021) · Information Sciences · doi:10.1016/j.ins.2021.12.021 · Cited by 27
Using transfer learning for diabetic retinopathy stage classification
Houby, E. M. F. E. (2021) · Applied Computing and Informatics · doi:10.1108/aci-07-2021-0191 · Cited by 22
Survey on deep learning applications in digital image security
Bao, Z., & Xue, R. (2021) · Optical Engineering · doi:10.1117/1.oe.60.12.120901 · Cited by 24
YOLOv5 Based Visual Localization For Autonomous Vehicles
Mseddi, W. S., Sedrine, M. A., & Attia, R. (2021) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco54536.2021.9616354 · Cited by 24
Stain transfer using Generative Adversarial Networks and disentangled features
Moghadam, A. Z., Azarnoush, H., Seyyedsalehi, S. A., & Havaei, M. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105219 · Cited by 23
Multi-scale kronecker-product relation networks for few-shot learning
Abdelaziz, M., & Zhang, Z. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11735-w · Cited by 22
FPGA-based Acceleration for Convolutional Neural Networks on PYNQ-Z2
Huynh, T. V. (2022) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/110136 · Cited by 23
Fine-tuned Xception for Image Classification on Tiny ImageNet
Benbrahim, H., & Behloul, A. (2021) · doi:10.1109/ai-csp52968.2021.9671150 · Cited by 22
Molitor, D. A., Kubik, C., Becker, M., Hetfleisch, R. H., Lyu, F., & Groche, P. (2021) · Journal of Materials Processing Technology · doi:10.1016/j.jmatprotec.2021.117484 · Cited by 24
Monitoring of Inland Water Levels by Satellite Altimetry and Deep Learning
Niño, F., Coggiola, C., Blumstein, D., Lasson, L., & Calmant, S. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3138329 · Cited by 22
Xu, H., Zhu, P., Luo, X., Xie, T., & Zhang, L. (2022) · Remote Sensing · doi:10.3390/rs14030564 · Cited by 22
Estimation of strengths of hybrid FR‐SCC by using deep‐learning and support vector regression models
Kına, C., Türk, K., & Tanyıldızı, H. (2022) · Structural Concrete · doi:10.1002/suco.202100622 · Cited by 22
Fine-grained identification of vehicle loads on bridges based on computer vision
Zhu, J., Li, X., & Zhang, C. (2022) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-022-00552-w · Cited by 22
Yuan, P., Qian, S., Zhai, Z., FernánMartínez, J., & Xu, H. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106679 · Cited by 21
Mehrotra, R., Agrawal, R. K., & Ansari, M. A. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11748-5 · Cited by 22
Generalized Knowledge Distillation via Relationship Matching
Ye, H.-J., Lu, S., & Zhan, D. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3160328 · Cited by 26
Multi-Agent Deep Q Network to Enhance the Reinforcement Learning for Delayed Reward System
Kim, K. (2022) · Applied Sciences · doi:10.3390/app12073520 · Cited by 22
Lee, H. H., & Kim, H. (2022) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29214 · Cited by 23
Image Classification of Parcel Boxes under the Underground Logistics System Using CNN MobileNet
Kim, M., Kwon, Y.-J., Kim, J., et al. (2022) · Applied Sciences · doi:10.3390/app12073337 · Cited by 22
Lamooki, S. R., Hajifar, S., Kang, J., et al. (2022) · Applied Ergonomics · doi:10.1016/j.apergo.2022.103732 · Cited by 22
Deep Reinforcement Learning for UAV Intelligent Mission Planning
Yue, L., Yang, R., Zhang, Y., Yu, L., & Wang, Z. (2022) · Complexity · doi:10.1155/2022/3551508 · Cited by 24
Deep learning-assisted wavefront correction with sparse data for holographic tomography
Lin, L.-C., Huang, C.-H., Chen, Y.-F., Chu, D. P., & Cheng, C.-J. (2022) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2022.107010 · Cited by 22
Malliga, S., Nandhini, P. S., & Kogilavani, S. V. (2022) · Information Technology And Control · doi:10.5755/j01.itc.51.1.29595 · Cited by 24
Farizhandi, A. A. K., Betancourt, O., & Mamivand, M. (2021) · Scientific Reports · doi:10.1038/s41598-022-08484-7 · Cited by 24
Lyakhov, P. A., Lyakhova, U. A., & Nagornov, N. N. (2022) · Cancers · doi:10.3390/cancers14071819 · Cited by 28
MPCCL: Multiview predictive coding with contrastive learning for person re-identification
Yin, J.-H., Xie, J.-Y., Ma, Z.-Y., & Guo, J. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108710 · Cited by 22
Ullah, H., Heyat, M. B. B., AlSalman, H., Khan, H. M., Akhtar, F., Gumaei, A., Mehdi, A., Muaad, A. Y. M., Islam, M. S., Ali, A., et al. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/3408501 · Cited by 22
RetractedHow AI Can Help in the Diagnostic Dilemma of Pulmonary Nodules
Fahmy, D. M., Kandil, H., Khelifi, A., Yaghi, M., Ghazal, M. A., Sharafeldeen, A., Mahmoud, A. H., & El-Baz, A. S. (2022) · Cancers · doi:10.3390/cancers14071840 · Cited by 22
A Survey of Dental Caries Segmentation and Detection Techniques
Majanga, V., & Viriri, S. (2022) · The Scientific World JOURNAL · doi:10.1155/2022/8415705 · Cited by 26
Liu, Y., Huff, O. R., Luo, B., Jin, G., & Simmons, J. (2022) · Geophysical Prospecting · doi:10.1111/1365-2478.13199 · Cited by 21
Arıcıoğlu, B., Uzun, S., & Kaçar, S. (2022) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2022.133306 · Cited by 22
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
