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 65,701–65,800 of 108,435
Tang, H., Cui, X., Zhu, J., Shen, L., Cai, J., & Zhu, B. (2023) · Prognostics and Health Management Conference (PHM) · doi:10.1109/phm58589.2023.00012 · Cited by 6
A Novel Spatiotemporal Analysis Framework for Air Pollution Episode Association in Puli, Taiwan
Yin, P.-Y. (2023) · Applied Sciences · doi:10.3390/app13095808 · Cited by 5
Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost
Yin, L., Liu, S., Fang, M., Huang, T., Menkovski, V., & Pechenizkiy, M. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i9.26297 · Cited by 10
CovC-ReDRNet: A Deep Learning Model for COVID-19 Classification
Zhu, H.-R., Zhu, Z.-Q., Wang, S.-H., & Zhang, Y.-D. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5030037 · Cited by 4
High-Resolution GAN Inversion for Degraded Images in Large Diverse Datasets
Wang, Y., Lin, C., Luo, D., et al. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i3.25371 · Cited by 5
Unknown Traffic Recognition Based on Multi-Feature Fusion and Incremental Learning
Liu, J., Wang, J., Yan, T., Qi, F., & Chen, G. (2023) · Applied Sciences · doi:10.3390/app13137649 · Cited by 5
An Introduction to Neural Networks
Aggarwal, C. C. (2023) · Springer eBooks · doi:10.1007/978-3-031-29642-0_1 · Cited by 6
InkGAN: Generative Adversarial Networks for Ink-And-Wash Style Transfer of Photographs
Yu, K., Wang, Y., Zeng, S., Liang, C., Bai, X., Chen, D., & Wang, W. (2023) · Advances in Artificial Intelligence and Machine Learning · doi:10.54364/aaiml.2023.1171 · Cited by 5
Deep Interactive Full Transformer Framework for Point Cloud Registration
Chen, G., Wang, M., Zhang, Q., Li, Y., Liu, T., & Yue, Y. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160863 · Cited by 5
From Coding To Curing. Functions, Implementations, and Correctness in Deep Learning
Angius, N., & Plebe, A. (2023) · Philosophy & Technology · doi:10.1007/s13347-023-00642-7 · Cited by 5
A Pattern Recognition Model: Hand Gestures Recognition using Convolutional Neural Networks
Reddy, D. A., Jyothi, V. E., Viswanath, J., et al. (2023) · International Conference on Sustainable Computing and Smart Systems (ICSCSS) · doi:10.1109/icscss57650.2023.10169433 · Cited by 5
Semisupervised Cross Domain Teacher–Student Mutual Training for Damaged Building Detection
Pan, J., Yin, P., Sun, X., Tan, J., & Li, W. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3293397 · Cited by 6
Detecting Laryngeal Cancer Lesions From Endoscopy Images Using Deep Ensemble Model
Bhattacharjee, R., Devi, K. S., & Vijaykanth, S. (2023) · International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT) · doi:10.1109/iconscept57958.2023.10170113 · Cited by 5
An improved image segmentation model of FCN based on residual network
Du, W., Yang, H., & Toe, T. T. (2023) · International Conference on Computer Vision, Image and Deep Learning (CVIDL) · doi:10.1109/cvidl58838.2023.10166778 · Cited by 5
Yoshimura, T., Manabe, K., & Sugimori, H. (2023) · Applied Sciences · doi:10.3390/app13148028 · Cited by 5
Lightweight Approaches to DNN Regression Error Reduction: An Uncertainty Alignment Perspective
Li, Z., Zhang, M., Xu, J., Yao, Y., Cao, C., Chen, T., Ma, X., & Lü, J. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00106 · Cited by 5
Synthesizing Trajectory Queries from Examples
Mell, S., Bastani, F., Zdancewic, S., & Bastani, O. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-37706-8_23 · Cited by 4
“Abstractly, these models can be thought of as providing interfaces to real-world data—e.g., they can provide object classes [47,30], detections [60,59], and trajectories [11,83,10].”
BackgroundThe Advance of GPTs and Language Model in Cyber Security
Gao, M. (2023) · Highlights in Science Engineering and Technology · doi:10.54097/hset.v57i.10001 · Cited by 5
Asadzadeh, M. Z., Roppert, K., & Raninger, P. (2023) · Materials · doi:10.3390/ma16145013 · Cited by 5
Lu, Y., Pei, J., Wang, X., et al. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3296514 · Cited by 5
CSA Based Radix-4 Gemmini Systolic Array for Machine Learning Applications
Shafique, M. A., Inayat, K., & Lee, J. (2023) · International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies (HEART) · doi:10.1145/3597031.3597056 · Cited by 5
Determination of Multi-Component Failure in Automotive System Using Deep Learning
O’Donnell, J., & Yoon, H. (2023) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4063003 · Cited by 5
Handwritten Character Recognition Using Machine Learning
Kumar, A., Srivastava, K., Saraswat, A., et al. (2023) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite57410.2023.10182858 · Cited by 5
Transfer Learning for Mosquito Classification Using VGG16
Siddiqui, A. A. I., & Kayte, C. (2023) · Advances in intelligent systems research/Advances in Intelligent Systems Research · doi:10.2991/978-94-6463-196-8_36 · Cited by 5
Takagi, A., Ichikawa, R., Miyagawa, T., Song, J., Yonezu, A., & Nagatsuka, H. (2023) · Polymer Testing · doi:10.1016/j.polymertesting.2023.108161 · Cited by 5
Investigation of IBVS control method utilizing vanishing vector subject to spatial constraint
Cao, C., Ouyang, Q., Su, H., et al. (2023) · Measurement · doi:10.1016/j.measurement.2023.113376 · Cited by 5
Efficient Deep Neural Network Training Techniques for Overfitting Avoidance
Sabiri, B., Asri, B. E., & Rhanoui, M. (2023) · Lecture notes in business information processing · doi:10.1007/978-3-031-39386-0_10 · Cited by 6
Broad Transfer Learning Network based Li-ion battery lifetime prediction model
Kuo, P., Tseng, Y., Luan, P., & Yau, H. (2023) · Energy Reports · doi:10.1016/j.egyr.2023.07.025 · Cited by 6
Hadrian, A., Vainshtein, R., Shapira, B., & Rokach, L. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3296532 · Cited by 4
Accuracy Is Not Enough: Optimizing for a Fault Detection Delay
Šprogar, M., & Verber, D. (2023) · Mathematics · doi:10.3390/math11153369 · Cited by 5
DySuse: Susceptibility estimation in dynamic social networks
Shi, Y., Zhou, J., & Zhang, C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121042 · Cited by 5
Revisiting Data Augmentation in Model Compression: An Empirical and Comprehensive Study
Yu, M., Zhang, L., & Ma, K. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191736 · Cited by 5
Entity-level Sentiment Analysis in Contact Center Telephone Conversations
Fu, X.-Y., Chen, C., Laskar, M. T. R., Gardiner, S., Hiranandani, P., & Shashi Bhushan, T. N. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-industry.49 · Cited by 11
Exploring the optimization of autoencoder design for imputing single-cell RNA sequencing data
Xi, N. M., & Li, J. J. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.07.041 · Cited by 8
Lu, W., Zhang, D., Ren, Q., Qi, T., & He, C. (2023) · Landscape Ecology · doi:10.1007/s10980-023-01740-9 · Cited by 5
Seq-SVF: An unsupervised data-driven method for automatically identifying hidden governing equations
Wu, Z., Ye, H., Zhang, H., & Zheng, Y. (2023) · Computer Physics Communications · doi:10.1016/j.cpc.2023.108887 · Cited by 5
E-FPN: Evidential Feature Pyramid Network for Ship Classification
Dong, Y., Xu, K., Zhu, C., et al. (2023) · Remote Sensing · doi:10.3390/rs15153916 · Cited by 5
Zhao, E., Dong, L., & Dai, H. (2023) · Infrared Physics & Technology · doi:10.1016/j.infrared.2023.104852 · Cited by 5
Bockrath, S., Wachtler, J., Wenger, M., Schwarz, R., Pruckner, M., & Lorentz, V. (2023) · IEEE Space Computing Conference (SCC) · doi:10.1109/scc57168.2023.00023 · Cited by 5
Model Conversion via Differentially Private Data-Free Distillation
Liu, B., Wang, P., Li, S., Zeng, D., & Ge, S. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/243 · Cited by 7
AKGF: Automatic Kernel Generation for DNN on CPU-FPGA
Dong, D., Jiang, H., & Diao, B. (2023) · The Computer Journal · doi:10.1093/comjnl/bxad086 · Cited by 5
Feature enhancement guided network for yield estimation of high-density jujube
Cheng, F., Wei, J., Jiang, S., Chen, Q., Ru, Y., & Zhou, H. (2023) · Plant Methods · doi:10.1186/s13007-023-01066-2 · Cited by 5
Refined Division Features Based on Transformer for Semantic Image Segmentation
Li, T., Wei, Y., Liu, M., Yang, X., Zhang, Z., & Du, J. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/6358162 · Cited by 5
3D9C seismic data reconstruction with multi-scale convolution neural network
Tang, H., Cheng, S., Song, H., et al. (2023) · Journal of Applied Geophysics · doi:10.1016/j.jappgeo.2023.105163 · Cited by 5
Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz
Çelebi, S. B., & Emiroğlu, B. G. (2023) · Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi · doi:10.21597/jist.1275669 · Cited by 5
A Rotation & Scale Invariant CNN Model to Detect Humans in Disaster Area from Aerial View
Moury, K. S., Gauhar, N., & Ahsan, S. M. M. (2023) · International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM) · doi:10.1109/ncim59001.2023.10212911 · Cited by 5
Yu, C., Wang, M., Chen, S., Qiu, C., Zhang, Z., & Zhang, X. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105356 · Cited by 5
How to Prevent the Continuous Damage of Noises to Model Training?
Yu, X., Jiang, Y., Shi, T., Feng, Z., Wang, Y., Song, M., & Sun, L. J. (2023) · 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/cvpr52729.2023.01160 · Cited by 5
Deep Neural Network Based Object Detection Algorithm With optimized Detection Head for Small Targets
Zhu, Y., Dong, E., Tong, J., et al. (2023) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma57826.2023.10215969 · Cited by 5
Mao, M., Va, H., Lee, A., & Hong, M. (2023) · Applied Sciences · doi:10.3390/app13179505 · Cited by 6
HyperMatch: Noise-Tolerant Semi-Supervised Learning via Relaxed Contrastive Constraint
Zhou, B., Lü, J., Liu, K., et al. (2023) · 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/cvpr52729.2023.02300 · Cited by 5
Dionysus: Recovering Scene Structures by Dividing into Semantic Pieces
Wang, L., & Chen, L. (2023) · 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/cvpr52729.2023.01210 · Cited by 5
Cross-Modal Representation Learning
Yao, Y., Liu, Z., Lin, Y., & Sun, M. (2023) · Springer eBooks · doi:10.1007/978-981-99-1600-9_7 · Cited by 5
Model-Agnostic Federated Learning
Mittone, G., Riviera, W., Colonnelli, I., Birke, R., & Aldinucci, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-39698-4_26 · Cited by 10
HVP-Net: A Hybrid Voxel- and Point-Wise Network for Place Recognition
Chen, L., Kong, H., Wang, H., Yang, W., Lou, J. J., Xu, F., & Ren, M. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3308116 · Cited by 6
Post-Training Quantization for Vision Transformer in Transformed Domain
Feng, K., Chen, Z., Gao, F., Wang, Z., Xu, L., & Lin, W. (2023) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme55011.2023.00252 · Cited by 5
Huang, W., Zhu, G., Huang, Q., Chen, Z., Chen, J., & Li, J. (2023) · International Conference on Advanced Robotics and Mechatronics (ICARM) · doi:10.1109/icarm58088.2023.10218404 · Cited by 7
Marzouk, M. A., & Elkholy, M. (2023) · Journal of Advances in Information Technology · doi:10.12720/jait.14.4.838-845 · Cited by 5
Aghajanpoor, A., Sorouri, M., & Sharifi, A. (2023) · IEEE International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie51358.2023.10227980 · Cited by 5
Al-Atroshi, S. J. A., & Ali, A. M. (2023) · Traitement du signal · doi:10.18280/ts.400429 · Cited by 5
Texture-based Clustering of Archaeological Textile Images
Gigilashvili, D., Nguyen, H. T., Gulbrandsen, C. F., et al. (2023) · Archiving Conference · doi:10.2352/issn.2168-3204.2023.20.1.29 · Cited by 5
Deep-view linguistic and inductive learning (DvLIL) based framework for Image Retrieval
Ahmed, I., Iltaf, N., Khan, Z., & Zia, U. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119641 · Cited by 6
Structure preserving projections learning via low-rank embedding for image classification
Cai, M., Wan, M., Yang, G., Yang, Z., Zheng, H., Tan, H., & Tang, M. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119636 · Cited by 5
Yang, J., Yang, J., Luo, L., Wang, Y., Wang, S., & Liu, J. (2023) · Electronics · doi:10.3390/electronics12173711 · Cited by 8
Ske-Fi: Estimating Hand Poses via RF Vision Under Low Contrast and Occlusion
Xu, J. W., Yang, B., Chen, Z., Lv, P., Zhang, S., & Luo, J. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3312316 · Cited by 5
Bone tumor examination based on FCNN-4s and CRF fine segmentation fusion algorithm
Wu, S., Bai, X., Cai, L., Wang, L., Zhang, X., Ke, Q., & Huang, J. (2023) · Journal of bone oncology · doi:10.1016/j.jbo.2023.100502 · Cited by 6
MuCon: Multi-channel convolution for targeted sentiment classification
Verma, S., Kumar, A., & Sharan, A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16586-1 · Cited by 5
Mountain Forest Type Classification Based on One-Dimensional Convolutional Neural Network
Bai, M., Peng, P., Zhang, S., et al. (2023) · Forests · doi:10.3390/f14091823 · Cited by 5
Lafrenière, M., Valdés, G., & Descovich, M. (2023) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.14146 · Cited by 5
Optimizing Transformer for Large-Hole Image Inpainting
Li, Z., & Wang, Y. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10222218 · Cited by 5
Application of Hyperspectral Imaging to Identify Pine Seed Varieties
Ma, J., Pang, L., Guo, Y., Wang, J., Ma, J., He, F. J., & Yan, L. (2023) · Journal of Applied Spectroscopy · doi:10.1007/s10812-023-01614-7 · Cited by 5
Real-world efficient fall detection: Balancing performance and complexity with FDGA workflow
Zeng, G., Zeng, B., & Hu, H. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103832 · Cited by 5
Multi-label Video Classification for Underwater Ship Inspection
Azad, M. A., Mohammed, A. M., Waszak, M., Elvesæter, B., & Ludvigsen, M. (2023) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.1109/oceanslimerick52467.2023.10244578 · Cited by 7
Deep learning classification of systemic sclerosis from multi-site photoplethysmography signals
Iqbal, S., Bacardit, J., Griffiths, B., & Allen, J. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1242807 · Cited by 7
Ohashi, K., Nakanishi, K., Yasui, Y., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3314750 · Cited by 5
Derin öğrenme teknikleri ile elmada (Granny Smith) kusur tespiti
Ünal, Z., Кизилдениз, Т., ÖZDEN, M., Aktaş, H., & Karagöz, Ö. (2023) · Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi · doi:10.28948/ngumuh.1250012 · Cited by 5
Jiang, Y., & Yin, G. (2023) · Remote Sensing · doi:10.3390/rs15184514 · Cited by 5
HammerDodger: A Lightweight Defense Framework against RowHammer Attack on DNNs
Gongye, C., Luo, Y., Xu, X., et al. (2023) · Proceedings - ACM IEEE Design Automation Conference · doi:10.1109/dac56929.2023.10247671 · Cited by 5
Hardening RGB-D object recognition systems against adversarial patch attacks
Zheng, Y., Demetrio, L., Ciná, A. E., Feng, X., Xia, Z., Jiang, Z., Demontis, A., Biggio, B., & Roli, F. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119701 · Cited by 5
SCH: Symmetric Consistent Hashing for cross-modal retrieval
Ni, H., Fang, X., Kang, P., et al. (2023) · Signal Processing · doi:10.1016/j.sigpro.2023.109255 · Cited by 5
A plum selection system that uses a multi-class Convolutional Neural Network (CNN)
Fonseca, Y., Bautista, C., Beainy, C. E. P., & Parra, C. (2023) · Journal of Agriculture and Food Research · doi:10.1016/j.jafr.2023.100793 · Cited by 6
Jiang, L., Sun, S., Ge, J., Zhang, H., & Kang, W. (2023) · IEEE Non-Volatile Memory Systems and Applications Symposium (NVMSA) · doi:10.1109/nvmsa58981.2023.00026 · Cited by 5
IoT Enabled Real Time Appearance System using AI Camera and Deep Learning for Student Tracking
Verma, P. K., Sharma, V., Kumar, P., Sharma, S., Chaudhary, S., & Preety, P. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i6s.6885 · Cited by 14
A deep learning-based mobile app system for visual identification of tomato plant disease
Wang, A., & Shabrina, N. H. (2023) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v13i6.pp6992-7004 · Cited by 7
Vertical Layering of Quantized Neural Networks for Heterogeneous Inference
Hai, W., He, R., Tan, H., Qi, X., & Huang, K. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3319045 · Cited by 7
Texture representation via analysis and synthesis with generative adversarial networks
Lin, J., Xu, Z., Gaurav Sharma, A., & Pappas, T. N. (2023) · e-Prime - Advances in Electrical Engineering Electronics and Energy · doi:10.1016/j.prime.2023.100286 · Cited by 8
SPAT: Semantic-Preserving Adversarial Transformation for Perceptually Similar Adversarial Examples
Swain, S. K., Kumar, V., Kim, D. S., & Bai, G. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230525 · Cited by 4
Dual-path parallel hierarchical diagnostic model of glioma based on pathomorphological feature
Fang, L., & Jiang, Y. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105559 · Cited by 5
Enhancing accessibility of web-based SVG buttons: An optimization method and best practices
Zhang, M., Yan, Z., Gao, G., & Liu, H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121883 · Cited by 7
3D Biological/Biomedical Image Registration with enhanced Feature Extraction and Outlier Detection
Hamzehei, S., Bai, J., Raimondi, G. M., Tripp, R., Ostroff, L. E., & Nabavi, S. (2023) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3584371.3612965 · Cited by 4
Sirico, A., & Herber, D. R. (2023) · Journal of Mechanical Design · doi:10.1115/1.4063659 · Cited by 9
Crop leaf disease detection for beans using ensembled-convolutional neural networks
Sahu, P., Chug, A., & Singh, A. P. (2023) · International Journal of Food Engineering · doi:10.1515/ijfe-2023-0055 · Cited by 5
Deformable element-wise dynamic convolution
Kim, W., Tanaka, M., Sasaki, Y., & Okutomi, M. (2023) · Journal of Electronic Imaging · doi:10.1117/1.jei.32.5.053029 · Cited by 5
Cigarette Authentication by Using Deep Learning-Based Anomaly Detection of Adhesive Mark Images
Liang, Z., Li, D., He, B., et al. (2023) · International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc58118.2023.10270366 · Cited by 5
V-shaped neural network structure based on multi-scale features for image denoising
Zhang, J., Liu, S., Zhang, Z., Shao, M., & Li, Y. (2023) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2023.103952 · Cited by 5
Higher efficient YOLOv7: a one-stage method for non-salient object detection
Dong, C., Tang, Y., & Zhang, L. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17185-w · Cited by 5
An improved small target traffic sign detection algorithm for YOLOv5s
Zou, W., Li, W., Li, G., et al. (2023) · International Conference on Electronics and Communication, Network and Computer Technology (ECNCT) · doi:10.1109/ecnct59757.2023.10281017 · Cited by 5
Xu, T., Zhao, Z., & Wu, J. Y. (2023) · Applied Sciences · doi:10.3390/app132011374 · Cited by 7
Li, J., Zhang, S., Qiang, Y.-R., & Zhou, Q.-Y. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107584 · Cited by 5
Dynamic Gesture Recognition Based on Two-Scale 3-D-ConvNeXt
Hao, S., Fu, M., Liu, X., & Zheng, B. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3324479 · Cited by 6
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
