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 62,601–62,700 of 108,435
SSAE and GRU Based Joint Modeling for Nonlinear Distributed Parameter Systems
Ai, L., Gan, J., Feng, X., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3206950 · Cited by 6
Mocz, V., Vaziri-Pashkam, M., Chun, M. M., & Xu, Y. (2022) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01916 · Cited by 6
Interpolation graph convolutional network for 3D point cloud analysis
Liu, Y., Yao, L., Li, B., Sammut, C., & Chang, X. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.23087 · Cited by 6
Aeroengine Working Condition Recognition Based on MsCNN-BiLSTM
Zheng, J., Peng, J., Wang, W., et al. (2022) · Sensors · doi:10.3390/s22187071 · Cited by 6
Not all edges are peers: Accurate structure-aware graph pooling networks
Yu, H., Yuan, J., Yao, Y., & Wang, C. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.09.004 · Cited by 5
Fanizzi, A., Scognamillo, G., Nestola, A., Bambace, S., Bove, S., Comes, M. C., Cristofaro, C., Didonna, V., Rito, A. D., Errico, A., et al. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.993395 · Cited by 7
CNN-based visible ingredient segmentation in food images for food ingredient recognition
Zhu, Z., & Dai, Y. (2022) · International Congress on Advanced Applied Informatics (IIAI-AAI) · doi:10.1109/iiaiaai55812.2022.00077 · Cited by 6
Time-frequency analysis-based deep interference classification for frequency hopping system
Xu, C., Ren, J., Yu, W., et al. (2022) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-022-00913-z · Cited by 6
Selective Layer Tuning and Performance Study of Pre-Trained Models Using Genetic Algorithm
Jeong, J.-C., Yu, G.-H., Song, M.-G., Vu, D. T., Anh, L. H., Jung, Y.-A., Choi, Y., Um, T., & Kim, J. Y. (2022) · Electronics · doi:10.3390/electronics11192985 · Cited by 6
Application of Convolutional Networks in Clothing Design from the Perspective of Deep Learning
Yi, C. (2022) · Scientific Programming · doi:10.1155/2022/6173981 · Cited by 6
Model Soups for Various Training and Validation Data
Suzuki, K., & Matsuzawa, T. (2022) · AI · doi:10.3390/ai3040048 · Cited by 7
SEDCN: An improved Deep & Cross Network Recommendation Algorithm based on SENET
Li, J., & Cheng, N. (2022) · IEEE/ACIS International Conference on Computer and Information Science (ICIS) · doi:10.1109/icis54925.2022.9882347 · Cited by 6
Classification of Layout vs. Relational Tables on the Web: Machine Learning with Rendered Pages
Haider, W., & Yeşilada, Y. (2022) · ACM Transactions on the Web · doi:10.1145/3555349 · Cited by 6
Physics aware analytics for accurate state prediction of dynamical systems
Mandal, A., Tiwari, Y., Panigrahi, P. K., et al. (2022) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2022.112670 · Cited by 6
SpiCoNET: A Hybrid Deep Learning Model to Diagnose COVID-19 and Pneumonia Using Chest X-Ray Images
Tümen, V. (2022) · Traitement du signal · doi:10.18280/ts.390409 · Cited by 7
DOA estimation using two independent convolutional neural networks with residual blocks
Huang, Q., & Fang, W. (2022) · Digital Signal Processing · doi:10.1016/j.dsp.2022.103765 · Cited by 6
A Lightweight Deep Learning RF Fingerprint Recognition Method
Feng, J., Tang, X., Zhang, B., et al. (2022) · International Conference on Communications, Information System and Computer Engineering (CISCE) · doi:10.1109/cisce55963.2022.9851177 · Cited by 6
AboElenein, N. M., Piao, S., & Zhang, Z. (2022) · Neural Processing Letters · doi:10.1007/s11063-022-10959-7 · Cited by 6
A Skeleton-Line-Based Graph Convolutional Neural Network for Areal Settlements’ Shape Classification
Li, Y., Lu, X., Yan, H., et al. (2022) · Applied Sciences · doi:10.3390/app121910001 · Cited by 6
Deep Learning Technology Applied to Medical Image Tissue Classification
Tsai, M.-J., & Tao, Y.-H. (2022) · Diagnostics · doi:10.3390/diagnostics12102430 · Cited by 9
Tensor-Based Learning for Detecting Abnormalities on Digital Mammograms
Tzortzis, I. N., Davradou, A., Rallis, I., Kaselimi, M., Makantasis, K., Doulamis, A. D., & Doulamis, N. D. (2022) · Diagnostics · doi:10.3390/diagnostics12102389 · Cited by 6
Neural Network-Based Underwater Object Detection off the Coast of the Korean Peninsula
Kim, W.-K., Bae, H. S., Son, S.-U., et al. (2022) · Journal of Marine Science and Engineering · doi:10.3390/jmse10101436 · Cited by 6
A Comparative Study of Engraved-Digit Data Augmentation by Generative Adversarial Networks
Abdulraheem, A., & Jung, I. Y. (2022) · Sustainability · doi:10.3390/su141912479 · Cited by 6
Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision
Zhan, J., Liu, J., Tang, W., et al. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3547834 · Cited by 6
Particleboard Surface Defect Inspection Based on Data Augmentation and Attention Mechanisms
Li, B., Xu, Z., Bian, E., et al. (2022) · International Conference on Automation and Computing (ICAC) · doi:10.1109/icac55051.2022.9911064 · Cited by 6
A Class-Incremental Detection Method of Remote Sensing Images Based on Selective Distillation
Ruan, H., Peng, J., Chen, Y., et al. (2022) · Symmetry · doi:10.3390/sym14102100 · Cited by 6
A Reconfigurable Deep Neural Network on Chip Design with Flexible Convolutional Operations
Chen, K.-C. J., & Liao, Y.-S. (2022) · International Workshop on Network on Chip Architectures (NoCArc) · doi:10.1109/nocarc57472.2022.9911283 · Cited by 7
Jarrett, W. A., Avramov-Zamurovic, S., & Esposito, J. M. (2022) · Optics Communications · doi:10.1016/j.optcom.2022.129069 · Cited by 6
Explainable AI and Its Applications in Healthcare
Sarkar, A. (2022) · Intelligent systems reference library · doi:10.1007/978-3-031-12807-3_6 · Cited by 6
A robust covariate‐invariant gait recognition based on pose features
Parashar, A., Parashar, A., & Shekhawat, R. S. (2022) · IET Biometrics · doi:10.1049/bme2.12103 · Cited by 6
Zhao, Q., Zhang, Y.-Q., Hou, R., et al. (2022) · Sensors · doi:10.3390/s22208015 · Cited by 6
Bonora, A., Bortolotti, G., Bresilla, K., et al. (2022) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2022.10.005 · Cited by 6
A deep learning-based recognition for dangerous objects imaged in X-ray security inspection device
Wei, Q., Ma, S., Tang, S.-J., et al. (2022) · Journal of X-Ray Science and Technology · doi:10.3233/xst-221210 · Cited by 6
Nursikuwagus, A., Munir, R., & Khodra, M. L. (2022) · Journal of Imaging · doi:10.3390/jimaging8110294 · Cited by 7
Automatic damage detection of bridge joints and road pavements by artificial neural networks ANNs
Gagliardi, V., Bella, F., Sansonetti, G., et al. (2022) · doi:10.1117/12.2636217 · Cited by 6
Machine learning algorithms to identify cluster randomized trials from MEDLINE and EMBASE
Al‐Jaishi, A. A., Taljaard, M., Al-Jaishi, M. D., Abdullah, S. S., Thabane, L., Devereaux, P. J., Dixon, S. N., & Garg, A. X. (2022) · Systematic Reviews · doi:10.1186/s13643-022-02082-4 · Cited by 6
CoreScore: a machine learning approach to assess legacy core condition
Fellgett, M. W., Hall, A., Harris, S., Damaschke, M., & Kingdon, A. (2022) · Geological Society London Special Publications · doi:10.1144/sp527-2021-200 · Cited by 7
Detection of Pneumonia and COVID-19 from Chest X-Ray Images Using Neural Networks and Deep Learning
Nimbhorkar, J. S., Aravind, K. S., Jeevesh, K., et al. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-4863-3_6 · Cited by 6
Carbon risk and return prediction: Evidence from the multi-CNN method
Tang, J., & Li, J. (2022) · Frontiers in Environmental Science · doi:10.3389/fenvs.2022.1035809 · Cited by 10
Application of Graph Structures in Computer Vision Tasks
Andriyanov, N. A. (2022) · Mathematics · doi:10.3390/math10214021 · Cited by 6
Localization of lung abnormalities on chest X-rays using self-supervised equivariant attention
D’Souza, G., Reddy, N., & Manjunath, K. N. (2022) · Biomedical Engineering Letters · doi:10.1007/s13534-022-00249-5 · Cited by 6
Osadebey, M. E., Pedersen, M., Kalra, M., et al. (2022) · Expert Systems · doi:10.1111/exsy.13176 · Cited by 6
Feliandra, Z. B., Khadijah, S., Rachmadi, M. F., et al. (2022) · International Conference on Advanced Computer Science and Information Systems · doi:10.1109/icacsis56558.2022.9923501 · Cited by 6
Transfer Learning-based Ensemble Deep Learning for Road Cracks Detection
Maarouf, A. A., & Hachouf, F. (2022) · International Conference on Advanced Aspects of Software Engineering · doi:10.1109/icaase56196.2022.9931581 · Cited by 4
Fast Semantic Image Segmentation for Autonomous Systems
Papaioannidis, C., Mademlis, I., & Pitas, I. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897582 · Cited by 6
A convolutional neural network framework for classifying inappropriate online video contents
Tanantong, T., & Yongwattana, P. (2022) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v12.i1.pp124-136 · Cited by 10
Skin Cancer Classification and Comparison of Pre-trained Models Performance using Transfer Learning
Singha, S., & Roy, P. (2022) · Journal of Information Systems Engineering and Business Intelligence · doi:10.20473/jisebi.8.2.218-225 · Cited by 6
Cheong, Y., Kwon, H., & Popa, B.-I. (2022) · Frontiers in Physics · doi:10.3389/fphy.2022.1021887 · Cited by 6
Classification of the Human Protein Atlas Single Cell Using Deep Learning
Alsubait, T., Taghreed, S., & Alhakami, H. (2022) · Applied Sciences · doi:10.3390/app122211587 · Cited by 6
Micro-Doppler signatures based human activity classification using Dense-Inception Neural Network
Nguyen, N., Pham, M., Le, V., et al. (2022) · International Conference on Advanced Technologies for Communications · doi:10.1109/atc55345.2022.9943046 · Cited by 6
Abed, A., Akrout, B., & Amous, I. (2022) · SN Computer Science · doi:10.1007/s42979-022-01467-5 · Cited by 6
Yang, H., Park, H., & Lee, K. (2022) · Axioms · doi:10.3390/axioms11120664 · Cited by 8
Efficient imaging and computer vision detection of two cell shapes in young cotton fibers
Graham, B. P., Park, J., Billings, G. T., et al. (2022) · Applications in Plant Sciences · doi:10.1002/aps3.11503 · Cited by 6
Deep Learning Research Directions in Medical Imaging
Simionescu, C., & Iftene, A. (2022) · Mathematics · doi:10.3390/math10234472 · Cited by 6
Human Randomness in the Rock-Paper-Scissors Game
Komai, T., Kurokawa, H., & Kim, S.-J. (2022) · Applied Sciences · doi:10.3390/app122312192 · Cited by 7
Liu, X., Flanagan, C., Fang, J., Lei, Y., McGrath, L., Wang, J., Guo, X., Guo, J., McGrath, H., & Han, Y. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e11761 · Cited by 6
Master-CAM: Multi-scale fusion guided by Master map for high-quality class activation maps
Zhou, X., Li, Y., Cao, G., et al. (2022) · Displays · doi:10.1016/j.displa.2022.102339 · Cited by 6
Gulyanon, S., Deepaisarn, S., Srisumarnk, C., Chiewnawintawat, N., Angkoonsawaengsuk, A., Laitrakun, S., Opaprakasit, P., Rakpongsiri, P., Meechamnan, T., & Sompongse, D. (2022) · International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) · doi:10.1109/isai-nlp56921.2022.9960277 · Cited by 7
Mitra, N., & Morshed, B. I. (2022) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon54665.2022.9965722 · Cited by 6
A review of methods for predicting DNA N6-methyladenine sites
Han, K., Wang, J., Wang, Y., Zhang, L., Yu, M., Xie, F., Zheng, D., Xu, Y., Ding, Y., & Wan, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac514 · Cited by 8
Sequential Characteristics Based Operators Disassembly Quantization Method for LSTM Layers
Wang, Y., Ma, Z., & Yang, Z. (2022) · Applied Sciences · doi:10.3390/app122412744 · Cited by 6
Spatio-Temporal Outdoor Lighting Aggregation on Image Sequences Using Transformer Networks
Lee, H., Homeyer, C., Herzog, R., et al. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01725-2 · Cited by 6
Deep Oral Cancer Lesion Segmentation with Heterogeneous Features
Huang, S.-Y., Chiou, C.-Y., Tan, Y.-S., et al. (2022) · IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE) · doi:10.1109/rasse54974.2022.9989871 · Cited by 6
Multi-Part Adaptive Graph Convolutional Network for Skeleton-Based Action Recognition
Wang, W., Xie, W. C., Tu, Z., Li, W., & Jin, L. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892727 · Cited by 6
Integrating Statistical and Machine Learning Approaches for Neural Classification
Sarmashghi, M., Jadhav, S. P., & Eden, U. T. (2022) · IEEE Access · doi:10.1109/access.2022.3221436 · Cited by 7
Automatic Generation of Spatial Accelerator for Tensor Algebra
Jia, L., Luo, Z., Lu, L., & Liang, Y. E. (2022) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2022.3209949 · Cited by 6
GPU-Acceleration of Affine Prediction in the Versatile Video Coding
Storch, I., Palomino, D., & Bampi, S. (2022) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas48785.2022.9937704 · Cited by 6
Highly-Efficient Binary Neural Networks for Visual Place Recognition
Ferrarini, B., Milford, M. J., McDonald-Maier, K. D., & Ehsan, S. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981978 · Cited by 6
Diagnosis of Lung Cancer Based on CT Scans Using Convolutional Neural Networks
Huang, H., Wang, M., Ye, Q., et al. (2022) · International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI) · doi:10.1109/icdacai57211.2022.00073 · Cited by 6
Local Attention Pyramid for Scene Image Generation
Shim, S.-H., Hyun, S., Bae, D.-H., et al. (2022) · 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/cvpr52688.2022.00762 · Cited by 6
Zhang, S., Wang, C., Huang, D. G., et al. (2022) · IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications · doi:10.1109/mape53743.2022.9935193 · Cited by 6
Analyzing GCN Aggregation on GPU
Kim, I., Jeong, J. H., Oh, Y., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3217222 · Cited by 6
Unsupervised Representation Learning for Binary Networks by Joint Classifier Learning
Kim, D., & Choi, J. (2022) · 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/cvpr52688.2022.00952 · Cited by 6
Defect detection method for key components of transmission line based on incremental learning
Yang, W. P., Hao, J., & Yu, J. (2022) · International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE) · doi:10.1109/icbaie56435.2022.9985841 · Cited by 6
Xie, J., Jiang, T., & Chen, X. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.11.280 · Cited by 7
Low Power Neural Network by Reducing SRAM Operating Voltage
Kozu, K., Tanabe, Y., Kitakami, M., & Namba, K. (2022) · IEEE Access · doi:10.1109/access.2022.3219208 · Cited by 7
The combination of transformer and CNN in computer vision
Gao, Y., Shi, S., Sun, Z., & Ling, C. (2022) · IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT) · doi:10.1109/iccasit55263.2022.9987025 · Cited by 7
ADC-Free ReRAM-Based In-Situ Accelerator for Energy-Efficient Binary Neural Networks
Kim, H., Jung, Y., & Kim, L. (2022) · IEEE Transactions on Computers · doi:10.1109/tc.2022.3224800 · Cited by 6
An Efficient Oceanic Eddy Identification Method With XBT Data Using Transformer
Zhang, H., Huang, B., Chen, G., et al. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3221113 · Cited by 6
Driver State Detection from In-Car Camera Images
Fusek, R., Sojka, E., Gaura, J., & Halman, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20716-7_24 · Cited by 6
GAN-Based Framework for Unified Estimation of Process-Induced Random Variation in FinFET
Park, T., Kwak, J., Ahn, H., Lee, J., Lim, J., Yu, S., Shin, C., & Moon, T. (2022) · IEEE Access · doi:10.1109/access.2022.3228552 · Cited by 6
Hu, X., & Zhu, C. (2022) · Sensors · doi:10.3390/s23010207 · Cited by 6
Chen, Y., Xiang, T., & Zhang, X. (2022) · IEEE Antennas and Wireless Propagation Letters · doi:10.1109/lawp.2022.3231961 · Cited by 6
Data Augmentation for Small Sample Iris Image Based on a Modified Sparrow Search Algorithm
Xiong, Q., Zhang, X., He, S., et al. (2022) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-022-00173-7 · Cited by 6
Unsupervised Content Based Image Retrieval Using Pre-Trained CNN and PCNN Features Extractors
(2022) · International journal of intelligent engineering and systems · doi:10.22266/ijies2023.0228.50 · Cited by 6
AMF-MSPF: A retrospective analysis with online object tracking algorithms
Khattak, A. S., Anjum, N., Khan, N., Mufti, M. R., & Ramzan, N. (2022) · Displays · doi:10.1016/j.displa.2022.102354 · Cited by 6
PNEUMONIA DETECTION USING DEEP LEARNING BASED ON CONVOLUTIONAL NEURAL NETWORK (CNN) MODEL
(2022) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets32669 · Cited by 6
Artificial intelligence as a potential tool for micro-histological analysis of herbivore diets
Bartolomé, J., Bonilla, C. C. Q., Peña, E. C. Q., & Dalerum, F. (2023) · European Journal of Wildlife Research · doi:10.1007/s10344-022-01640-4 · Cited by 6
KONDO, H. X., Iizuka, H., Masumoto, G., Kabaya, Y., Kanematsu, Y., & Takano, Y. (2023) · Biomolecules · doi:10.3390/biom13010137 · Cited by 6
Seismic deghosting using convolutional neural networks
Almuteri, K., & Sava, P. C. (2023) · Geophysics · doi:10.1190/geo2021-0521.1 · Cited by 6
SPRTracker: Learning Spatial-Temporal Pixel Aggregations for Multiple Object Tracking
Liu, J., Kong, J., Jiang, M., & Liu, T. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3236262 · Cited by 6
Lee, J., Jung, M., Lustig, N., & Lee, J. (2023) · Human Brain Mapping · doi:10.1002/hbm.26189 · Cited by 5
Manifold learning for coherent design interpolation based on geometrical and topological descriptors
Muñoz, D., Allix, O., Chinesta, F., et al. (2023) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2022.115859 · Cited by 6
Liu, X., Zhang, B., & Liu, N. (2023) · Machines · doi:10.3390/machines11010116 · Cited by 7
Optimized MobileNetV2 Based on Model Pruning for Image Classification
Xiao, P., Pang, Y., Feng, H., et al. (2022) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip56404.2022.10008829 · Cited by 6
Tire pattern classification based on ConvNeXt Network
Zhu, Y., Tao, M., Guo, L., et al. (2022) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip56404.2022.10008899 · Cited by 6
Huang, N., Huang, Z., Fu, C., et al. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3237588 · Cited by 6
Reinforcement learning based Linear quadratic Regulator for the Control of a Quadcopter
Kashyap, V., & Vepa, R. (2023) · AIAA SciTech Forum · doi:10.2514/6.2023-0014 · Cited by 6
Broberg, J. S., Chen, J., Jensen, A., et al. (2023) · Journal of Orthopaedic Research® · doi:10.1002/jor.25518 · Cited by 6
Deep Learning-Based Virtual Optical Image Generation and Its Application to Early Crop Mapping
Park, N.-W., Park, M.-G., Kwak, G.-H., & Hong, S. (2023) · Applied Sciences · doi:10.3390/app13031766 · Cited by 7
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
