The DOI matches its Crossref record, with a small difference in title or year; 31 of the 31 references it cites were found in a registry or an index; 2 of the 14 sources its claims cite were checked for support.
ImageNet classification with deep convolutional neural networks
Open accessAbstract
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
42 Own result · 21 Own interpretation · 7 No basis given · 4 No full text of the sources · 3 Sources not found · 2 Supported · 2 Not checked yet · 1 Own method
Every source the paper cites for this claim was checked and supports it.
In the paper
“This technique reduces complex co adaptations of neurons, since a neuron cannot rely on the presence of particular other neurons.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
What the sources say
- Supportedcites [12] 12. Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012). source
The paper states on p. 1 that dropout prevents complex co-adaptations by randomly omitting hidden units, so a hidden unit cannot rely on other hidden units being present. This directly supports the citing sentence. (LLM, conf=0.95)
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“There is, however, a very efficient version of model combination that only costs about a factor of two during training.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
What the sources say
- Supportedcites [12] 12. Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012). source
The paper describes dropout as an efficient way to combine many neural-network models (p. 1). In F.6 Training (p. 17), it reports that ImageNet training took roughly four days with dropout versus two days without it, supporting the approximate factor-of-two training cost. (LLM, conf=0.88)
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This scheme reduces our top-1 and top-5 error rates by 1.7% and 1.2%, respectively, as compared with a net with half as many kernels in each convolutional layer trained on one GPU.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.2. Training on multiple GPUs
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The size of our network made overfitting a significant problem, even with 1.2 million labeled training examples”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“So these data augmentation schemes are, in effect, computationally free.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 18
- Probably not a substantial claim. 26
- The quote only partly states this claim. 2
Create a free account to see these claims.
Create a free account to see these assumptions.
34 more claims
Create a free account to see all 82 claims and to verify the 3 references still open.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In the field of DL, the CNN is the most famous and commonly employed algorithm [30, 71–75].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [30]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Convolutional neural networks (CNNs), built for spatial data, have demonstrated performance comparable to that of specialists in cancer detection and tumor segmentation [15,16,17,18,19,20].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [16]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Deep CNN architectures with 8, even 22 layers [4], [33], can be useful even for CADe problems where the available training datasets are limited.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [4]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Fully convolutional neural networks (fCNNs) have provided state-of-the-art performance for object classification and image segmentation (1).”
From Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning (2018) · cites this paper as [1]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The first wave saw the emergence of deep learning as the dominant paradigm for analyzing high-dimensional biological data.”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [1]
Experimental — accuracy not yet measured
19 more claims about this paper
Create a free account to see every claim other papers make about this one.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
“we found that removing any convolutional layer (each of which contains no more than 1% of the model's parameters) resulted in inferior performance.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.5. Overall architecture
“The third convolutional layer has 384 kernels of size 3 × 3 × 256”
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,884
Self-citationLearning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,501
Self-citation(2010) · International Conference on Machine Learning · Cited by 13,995
Self-citationImproving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
Self-citationFukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759
LabelMe: A Database and Web-Based Tool for Image Annotation
Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0090-8 · Cited by 4,210
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196
Research works printed but not linked: 13
- Berg A., 2010, Large scale visual recognition challenge
- Deng J., 2009, CVPR09
- Krizhevsky A., 2009, Department of Computer Science
- Krizhevsky A., 2010, Convolutional deep belief networks on cifar-10. Unpublished manuscript
- Krizhevsky A., 2011, ESANN
- LeCun Y., 1990, Advances in Neural Information Processing Systems
- LeCun Y., 1985, Une procedure d'apprentissage pour reseau a seuil asymmetrique (a learning scheme for asymmetric threshold networks)
- 10.5555/1896300.1896315 · DOI
- Mensink T., 2012, Italy
- 10.5555/3104322.3104425 · DOI
- Rumelhart D.E., 1985, DTIC Document
- 10.5555/938980.939477 · DOI
- Werbos P., 1974, Beyond regression: New tools for prediction and analysis in the behavioral sciences
Cited by
Items 44,201–44,300 of 108,439
A lightweight network with attention decoder for real-time semantic segmentation
Wang, K., Yang, J., Yuan, S., & Li, M. (2021) · The Visual Computer · doi:10.1007/s00371-021-02115-4 · Cited by 14
An Automated Tomato Maturity Grading System Using Transfer Learning Based AlexNet
Das, P., Yadav, J. K. P. S., & Yadav, A. K. (2021) · Ingénierie des systèmes d information · doi:10.18280/isi.260206 · Cited by 15
Islam, M. M., & Baek, J.-H. (2021) · Applied Sciences · doi:10.3390/app11104549 · Cited by 16
Wang, Y., Yoo, S., Braun, J.-M., & Nadimi, E. S. (2021) · Journal of Real-Time Image Processing · doi:10.1007/s11554-021-01126-7 · Cited by 12
Application of Deep Learning Networks to Segmentation of Surface of Railway Tracks
Bojarczak, P., & Nowakowski, W. (2021) · Sensors · doi:10.3390/s21124065 · Cited by 14
The Agent Web Model: modeling web hacking for reinforcement learning
Erdődi, L., & Zennaro, F. M. (2021) · International Journal of Information Security · doi:10.1007/s10207-021-00554-7 · Cited by 21
Ohta, Y., Matsuzawa, H., Yamamoto, K., Enchi, Y., Kobayashi, T., & Ishida, T. (2021) · Radiography · doi:10.1016/j.radi.2021.05.002 · Cited by 16
Acceleration-Aware Fine-Grained Channel Pruning for Deep Neural Networks via Residual Gating
Huang, K., Chen, S., Li, B., Claesen, L., Yao, H., Chen, J., Jiang, X., Liu, Z., & Xiong, D. (2021) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2021.3093835 · Cited by 14
Neural Architecture Search Using Covariance Matrix Adaptation Evolution Strategy
Sinha, N., & Chen, K. (2023) · Evolutionary Computation · doi:10.1162/evco_a_00331 · Cited by 16
GAN-CL: Generative Adversarial Networks for Learning From Complementary Labels
Liu, J., Hang, H., Wang, B., Li, B., Wang, H., Tian, Y., & Shi, Y. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3089337 · Cited by 15
Machine Learning and Deep Learning in Genetics and Genomics
Wu, D., Karhade, D. S., Pillai, M., Jiang, M.-Z., Huang, L., Li, G., Cho, H., Roach, J. M., Li, Y., & Divaris, K. (2021) · Springer eBooks · doi:10.1007/978-3-030-71881-7_13 · Cited by 15
Abohamer, H., Elseifi, M. A., Dhakal, N., et al. (2021) · Journal of Transportation Engineering Part B Pavements · doi:10.1061/jpeodx.0000310 · Cited by 14
Image-Based TF Colorization With CNN for Direct Volume Rendering
Kim, S., Jang, Y., & Kim, S.-E. (2021) · IEEE Access · doi:10.1109/access.2021.3100429 · Cited by 14
Luo, J., Li, Y., Zhou, W., Gong, Z., Zhang, Z., & Yao, W. (2021) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2021.016737 · Cited by 15
Localization of defects in solar cells using luminescence images and deep learning
Abdullah‐Vetter, Z., Buratti, Y., Dwivedi, P., et al. (2021) · Conference record of the IEEE Photovoltaic Specialists Conference · doi:10.1109/pvsc43889.2021.9518702 · Cited by 14
Data synthesis method preserving correlation of features
Yang, W., & Nam, W. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108241 · Cited by 15
Sheng, M., Tang, W., Tang, J., Zhang, M., Gong, S., & Xing, W. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/4430886 · Cited by 14
Explainable Deep Learning for Covid-19 Detection Using Chest X-ray and CT-Scan Images
Mahmoudi, S. A., Stassin, S., Daho, M. E. H., Lessage, X., & Mahmoudi, S. (2021) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-72752-9_16 · Cited by 14
ELAA: An efficient local adversarial attack using model interpreters
Guo, S., Geng, S., Xiang, T., Liu, H., & Hou, R. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22680 · Cited by 17
Chen, W., Gu, Z., Liu, Z., Fu, Y., Ye, Z., Zhang, X., & Xiao, L. (2021) · Complexity · doi:10.1155/2021/6296811 · Cited by 15
RGB-D scene analysis in the NICU
Dosso, Y. S., Greenwood, K., Harrold, J., & Green, J. R. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104873 · Cited by 14
A Decreasing Scaling Transition Scheme from Adam to SGD
Zeng, K., Liu, J., Jiang, Z., & Xu, D. (2022) · Advanced Theory and Simulations · doi:10.1002/adts.202100599 · Cited by 17
Detection of wearing safety helmet for workers based on YOLOv4
Yunyun, L. I. U., & Jiang, W. (2021) · International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) · doi:10.1109/icceai52939.2021.00016 · Cited by 14
A CNN-based policy for optimizing continuous action control by learning state sequences
Huang, T., Li, M., Qin, X., & Zhu, W. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.10.004 · Cited by 14
Optimizing Winograd-Based Convolution with Tensor Cores
Liu, J., Yang, D., & Lai, J. (2021) · Proceedings of the International Conference on Parallel Processing · doi:10.1145/3472456.3472473 · Cited by 14
Cross-Domain Few-Shot Micro-Expression Recognition Incorporating Action Units
Dai, Y. Q., & Feng, L. (2021) · IEEE Access · doi:10.1109/access.2021.3120542 · Cited by 14
Sparsity-control ternary weight networks
Deng, X., & Zhang, Z. M. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.10.018 · Cited by 15
Free-Space Optical Neural Network Based on Optical Nonlinearity and Pooling Operations
Sadeghzadeh, H., Koohi, S., & Paranj, A. F. (2021) · IEEE Access · doi:10.1109/access.2021.3123230 · Cited by 14
Griffin: Rethinking Sparse Optimization for Deep Learning Architectures
Shin, J. H., Shafiee, A. R., Pedram, A., Abdel-Aziz, H. A., Li, L., & Hassoun, J. (2022) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca53966.2022.00068 · Cited by 17
Rahuja, N., & Valluru, S. K. (2021) · International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) · doi:10.1109/rteict52294.2021.9573692 · Cited by 14
Xie, L., Udupa, J. K., Tong, Y., Torigian, D. A., Huang, Z., Kogan, R. M., Wootton, D., Choy, K. R., Sin, S., Wagshul, M. E., et al. (2021) · Medical Physics · doi:10.1002/mp.15345 · Cited by 15
Formula-driven Supervised Learning with Recursive Tiling Patterns
Kataoka, H., Matsumoto, A., Yamada, R., Satoh, Y., Yamagata, E., & Inoue, N. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00455 · Cited by 15
Autonomous Multiple Tramp Materials Detection in Raw Coal Using Single-Shot Feature Fusion Detector
Li, D., Meng, G., Sun, Z., et al. (2021) · Applied Sciences · doi:10.3390/app12010107 · Cited by 14
Improving PM2.5 Concentration Forecast with the Identification of Temperature Inversion
Yin, P.-Y., Chang, R.-I., Day, R., Lin, Y.-C., & Hu, C. Y. (2021) · Applied Sciences · doi:10.3390/app12010071 · Cited by 14
Spatial temporal and channel aware network for video-based person re-identification
Hui, F., Zhang, K., Li, H., Wang, J., & Wang, Z. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104356 · Cited by 14
Deep Convolutional Neural Networks for Fish Weight Prediction from Images
Yang, Y., Xue, B., Jesson, L. K., et al. (2021) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1109/ivcnz54163.2021.9653412 · Cited by 14
Lyu, Z., Chen, Y., Hou, Y., & Zhang, C. (2022) · Digital Signal Processing · doi:10.1016/j.dsp.2022.103407 · Cited by 14
Neural networks for medical image segmentation
Radjabov, S., Khashimov, A., Urinov, E., et al. (2021) · International Conference on Information Science and Communications Technologies (ICISCT) · doi:10.1109/icisct52966.2021.9670147 · Cited by 14
An End-Cloud Collaborated Framework for Transferable Non-Intrusive Load Monitoring
Chen, C., Geng, G., Yu, H., et al. (2021) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2021.3132929 · Cited by 14
A novel lightweight bilateral segmentation network for detecting oil spills on the sea surface
Chen, Y., Sun, Y., Yu, W., et al. (2022) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2022.113343 · Cited by 14
MigSpike: A Migration Based Algorithms and Architecture for Scalable Robust Neuromorphic Systems
Dang, K. N., Doan, N. A. V., & Abdallah, A. B. (2021) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2021.3136028 · Cited by 14
Deep learning of early brain imaging to predict post-arrest electroencephalography
Elmer, J., Liu, C., Pease, M. W., et al. (2022) · Resuscitation · doi:10.1016/j.resuscitation.2022.01.004 · Cited by 14
Arabic Handwriting Recognition System Based on Genetic Algorithm and Deep CNN Architectures
Miloud, K., Lakhdar, A. M., & Ilyas, B. R. (2021) · International Conference on Decision Aid Sciences and Application (DASA) · doi:10.1109/dasa53625.2021.9682380 · Cited by 14
Pathak, D., & Raju, U. S. N. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6851 · Cited by 14
Salient object based visual sentiment analysis by combining deep features and handcrafted features
Sowmyayani, S., & Rani, P. A. J. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-11982-5 · Cited by 14
Improving adversarial robustness of Bayesian neural networks via multi-task adversarial training
Chen, X., Liu, C., Zhao, Y., Jia, Z., & Jin, G. (2022) · Information Sciences · doi:10.1016/j.ins.2022.01.051 · Cited by 15
Maqsood, M., Ghazanfar, M. A., Mehmood, I., Hwang, E., & Rho, S. (2022) · Journal of Signal Processing Systems · doi:10.1007/s11265-022-01742-x · Cited by 14
Chen, P., Gao, Y., & Ma, A. J. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00085 · Cited by 14
Handling partially labeled network data: A semi-supervised approach using stacked sparse autoencoder
Aouedi, O., Piamrat, K., & Bagadthey, D. (2022) · Computer Networks · doi:10.1016/j.comnet.2021.108742 · Cited by 15
Attentive Octave Convolutional Capsule Network for Medical Image Classification
Zhang, H., Li, Z., Zhao, H., Li, Z., & Zhang, Y. (2022) · Applied Sciences · doi:10.3390/app12052634 · Cited by 14
UnCNN: A New Directed CNN Model for Isolated Arabic Handwritten Characters Recognition
Khayati, M. E., & Elkettani, Y. (2022) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-022-06652-5 · Cited by 16
Liu, D., Liu, J., Pei-xin, Y., & Yu, F. W. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/8172466 · Cited by 14
Gupta, S. S., Hossain, S., & Kim, K. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116998 · Cited by 14
Zhu, L., Yan, D., Zhang, Z., & Chi, G. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/9038992 · Cited by 20
Research on Vehicle Lane Change Warning Method Based on Deep Learning Image Processing
Zhang, Q., Sun, Z., & Shu, H. Y. (2022) · Sensors · doi:10.3390/s22093326 · Cited by 14
Real-time defect detection and classification on wood surfaces using deep learning
Mohsin, M., Balogun, O. S., Haataja, K., & Toivanen, P. (2022) · Electronic Imaging · doi:10.2352/ei.2022.34.10.ipas-382 · Cited by 19
Transfer learning approach in deep neural networks for uterine fibroid detection
Sundar, S., & Subramanian, S. (2022) · International Journal of Computational Science and Engineering · doi:10.1504/ijcse.2022.120788 · Cited by 17
Knowledge Graph-Based Enhanced Transformer for Metro Individual Travel Destination Prediction
Chi, H., Wang, B., Ge, Q., & Huo, G. (2022) · Journal of Advanced Transportation · doi:10.1155/2022/8030690 · Cited by 15
Dog Skin Disease Recognition Using Image Segmentation and GPU Enhanced Convolutional Neural Network
Habal, B. G. M., Tiong, P. E. S., Pasatiempo, J. R., et al. (2021) · IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) · doi:10.1109/hnicem54116.2021.9731885 · Cited by 14
Classification of Cervical Cytology Overlapping Cell Images with Transfer Learning Architectures
Mulmule, P. V., & Kanphade, R. D. (2022) · Biomedical & Pharmacology Journal · doi:10.13005/bpj/2364 · Cited by 14
Dual attention and dual fusion: An accurate way of image-based geo-localization
Yuan, Y., Sun, B., & Liu, G. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.05.013 · Cited by 14
The Face Inversion Effect in Deep Convolutional Neural Networks
Fang, T., Xie, H., Song, Y., et al. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.854218 · Cited by 14
Driver Drowsiness Detection and Alert System Development Using Object Detection
Wu, J.-D., & Chang, C.-H. (2022) · Traitement du signal · doi:10.18280/ts.390211 · Cited by 14
Zhao, C., Zhao, X., Zhao, L., & Zhang, Q. (2022) · Sustainability · doi:10.3390/su14116829 · Cited by 14
Wang, J., Tang, L., Wang, C., et al. (2022) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2022.121463 · Cited by 14
Material measurement units for a circular economy: Foundations through a review
Zocco, F., McLoone, S., & Smyth, B. M. (2022) · Sustainable Production and Consumption · doi:10.1016/j.spc.2022.05.022 · Cited by 16
Li, F., Zhou, L., Wang, Y., Chen, C., Yang, S., Shan, F., & Liu, L. (2022) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-21-1117 · Cited by 17
SHREC 2022: Fitting and recognition of simple geometric primitives on point clouds
Romanengo, C., Raffo, A., Biasotti, S., et al. (2022) · Computers & Graphics · doi:10.1016/j.cag.2022.07.004 · Cited by 14
Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images
Salanitri, F. P., Bellitto, G., Palazzo, S., Irmakci, I., Wallace, M. B., Bolan, C. W., Engels, M. M. L., Hoogenboom, S. A. M., Aldinucci, M., Bağcı, U., et al. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871547 · Cited by 16
Research on a localization method of multiple unknown gamma radioactive sources
Hu, X., Huo, J., Wang, J., Hu, L., & Xiao, Y. (2022) · Annals of Nuclear Energy · doi:10.1016/j.anucene.2022.109302 · Cited by 13
Efficient Compact Bilinear Pooling via Kronecker Product
Yu, T., Cai, Y., & Li, P. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20225 · Cited by 16
Obodoekwe, E., Fang, X., & Lu, K. (2022) · Electronics · doi:10.3390/electronics11142128 · Cited by 14
RetractedResearch on artificial intelligence of accounting information processing based on image processing
Tian, J., & Li, L. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022391 · Cited by 14
A Bayesian Approach for Joint Discriminative Dictionary and Classifier Learning
Zhou, W. J., Wu, Y. H., Li, J., Wang, M., & Zhang, H. (2022) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2022.3170443 · Cited by 15
Ajala, S., Jalajamony, H. M., Nair, M., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-16114-5 · Cited by 14
Schuurmans, M., Alves, N., Vendittelli, P., Huisman, H. J., & Hermans, J. J. (2022) · Cancers · doi:10.3390/cancers14143498 · Cited by 16
Nsugbe, E., Samuel, O. W., Asogbon, M. G., & Li, G. (2022) · IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) · doi:10.1109/metroind4.0iot54413.2022.9831741 · Cited by 17
DeepDA-Ace: A Novel Domain Adaptation Method for Species-Specific Acetylation Site Prediction
Liu, Y., Wang, Q., & Xi, J. (2022) · Mathematics · doi:10.3390/math10142364 · Cited by 14
Pham, T.-T.-H., Nguyen, H.-P., Luu, T.-N., et al. (2022) · Journal of Biomedical Optics · doi:10.1117/1.jbo.27.7.075002 · Cited by 14
Ding, H., Li, F., Chen, X., Ma, J., Nie, S., Ye, R., & Yuan, C. (2022) · Cells · doi:10.3390/cells11152394 · Cited by 16
A Comparative Study of in-Database Inference Approaches
Lin, Q., Wu, S., Zhao, J., Dai, J., Li, F., & Chen, G. (2022) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde53745.2022.00180 · Cited by 15
Protein secondary structure assignment using residual networks
Antony, J. V., Koya, R., Pournami, P. N., Nair, G. G., & Balakrishnan, J. P. (2022) · Journal of Molecular Modeling · doi:10.1007/s00894-022-05271-z · Cited by 15
Working Condition Recognition Based on Transfer Learning and Attention Mechanism for a Rotary Kiln
Hu, Y., Zheng, W., Wang, X., et al. (2022) · Entropy · doi:10.3390/e24091186 · Cited by 14
Efficient CNN Architecture Design Guided by Visualization
Zhang, L., Shen, H., Luo, Y., Cao, X. G., Pan, L., Wang, T., & Feng, Q. (2022) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme52920.2022.9859729 · Cited by 15
A deep learning-based method for cervical transformation zone classification in colposcopy images
Cao, Y., Ma, H., Fan, Y., Liu, Y., Zhang, H., Cao, C., & Yu, H. (2022) · Technology and Health Care · doi:10.3233/thc-220141 · Cited by 17
Deep learning based liquid level extraction from video observations of gas–liquid flows
Olbrich, M., Riazy, L., Kretz, T., Leonard, T., van Putten, D. S., Bär, M., Oberleithner, K., & Schmelter, S. (2022) · International Journal of Multiphase Flow · doi:10.1016/j.ijmultiphaseflow.2022.104247 · Cited by 16
Token Selection is a Simple Booster for Vision Transformers
Zhou, D., Hou, Q., Yang, L., Jin, X., & Feng, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3208922 · Cited by 15
General object-based features account for letter perception
Janini, D., Hamblin, C. J., Deza, A., & Konkle, T. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010522 · Cited by 18
Hyperparameter Tuning for Medicare Fraud Detection in Big Data
Hancock, J., & Khoshgoftaar, T. M. (2022) · SN Computer Science · doi:10.1007/s42979-022-01348-x · Cited by 15
Handling missing data in well-log curves with a gated graph neural network
Jiang, C., Zhang, D., & Chen, S. (2022) · Geophysics · doi:10.1190/geo2022-0028.1 · Cited by 14
Keep the head in the right place: Face-body interactions in inferior temporal cortex
Zafirova, Y., Cui, D., Raman, R., & Vogels, R. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119676 · Cited by 13
PipeBERT: High-throughput BERT Inference for ARM Big.LITTLE Multi-core Processors
Chang, H.-Y., Mozafari, S. H., Chen, C., Clark, J. J., Meyer, B. H., & Gross, W. J. (2023) · Journal of Signal Processing Systems · doi:10.1007/s11265-022-01814-y · Cited by 14
Deep-Learning-Based Stroke Screening Using Skeleton Data from Neurological Examination Videos
Lee, T., Jeon, E., Jung, J., & Lee, M. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12101691 · Cited by 14
Convolutional Neural Network Model Based on 2D Fingerprint for Bioactivity Prediction
Hamza, H., Bengherbia, B., Saeed, F., Salim, N. B., Nafea, I. T., Toubal, A., & Nasser, M. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232113230 · Cited by 15
Taxonomic resolution of coral image classification with Convolutional Neural Network
Reshma, B., Rahul, B., Sreenath, K. R., Joshi, K. K., & George, G. (2022) · Aquatic Ecology · doi:10.1007/s10452-022-09988-0 · Cited by 17
Hybrid Deep Learning Models for Multi-classification of Tumour from Brain MRI
Munira, H. A., & Islam, M. S. (2022) · Journal of Information Systems Engineering and Business Intelligence · doi:10.20473/jisebi.8.2.162-174 · Cited by 17
ClueDepth Grasp: Leveraging positional clues of depth for completing depth of transparent objects
Hong, Y., Chen, J., Cheng, Y., et al. (2022) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2022.1041702 · Cited by 14
A Semi-Supervised Inspection Approach of Textured Surface Defects under Limited Labeled Samples
He, Y., Wen, X., & Xu, J. (2022) · Coatings · doi:10.3390/coatings12111707 · Cited by 14
Denız, E., Sobahi, N. M., Omar, N., Şengür, A., & Acharya, U. R. (2022) · Health Information Science and Systems · doi:10.1007/s13755-022-00201-y · Cited by 14
Automated Person Identification from Hand Images using Hierarchical Vision Transformer Network
Ebrahimian, Z., Mirsharji, S. A., Toosi, R., & Akhaee, M. A. (2022) · International Conference on Computer and Knowledge Engineering · doi:10.1109/iccke57176.2022.9960079 · Cited by 14
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
