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 58,901–59,000 of 108,438
SAR-PAA: A Physically Adversarial Attack Approach Against SAR Intelligent Target Recognition
Ma, Y., Pei, J., Huo, W., et al. (2024) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2024.3456750 · Cited by 7
Boiko, D. A., Arkhipova, D. M., & Ananikov, V. P. (2024) · Small · doi:10.1002/smll.202403423 · Cited by 9
Fang, C., Yuan, G., Zheng, Z., Zhong, Q., & Duan, K. (2024) · Hydrology and earth system sciences · doi:10.5194/hess-28-4085-2024 · Cited by 9
Goodman, S., BenYishay, A., & Runfola, D. M. (2024) · Remote Sensing · doi:10.3390/rs16183411 · Cited by 10
A deep learning method for contactless emotion recognition from ballistocardiogram
Yu, X., Zou, Y., Mou, X., Li, S., Bai, Z., Du, L., Li, Z., Wang, P., Chen, X., Li, X., et al. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106891 · Cited by 7
Polat, C., Kurban, H., & Kurban, M. (2024) · Computational Materials Science · doi:10.1016/j.commatsci.2024.113366 · Cited by 8
Calibration of Typhoon Track Forecasts Based on Deep Learning Methods
Tao, C., Wang, Z., Tian, Y., Han, Y., Wang, K., Li, Q., & Zuo, J. (2024) · Atmosphere · doi:10.3390/atmos15091125 · Cited by 7
Logarithmic Lenses: Exploring Log RGB Data for Image Classification
Maxwell, B. A., Singhania, S., Patel, A., Kumar, R., Fryling, H., Li, S., Sun, H., He, P., & Li, Z. (2024) · 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/cvpr52733.2024.01654 · Cited by 9
F3Loc: Fusion and Filtering for Floorplan Localization
Chen, C., Wang, R., Vogel, C., & Pollefeys, M. (2024) · 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/cvpr52733.2024.01707 · Cited by 8
Evolutionary neural architecture search for automated MDD diagnosis using multimodal MRI imaging
Li, T., Hou, N., Yu, J., Zhao, Z., Sun, Q., Chen, M., Yao, Z., Ma, S., Zhou, J., & Hu, B. (2024) · iScience · doi:10.1016/j.isci.2024.111020 · Cited by 9
An Innovative Hybrid Model for Automatic Detection of White Blood Cells in Clinical Laboratories
Aksoy, A. (2024) · Diagnostics · doi:10.3390/diagnostics14182093 · Cited by 7
Data-Driven Digital Twins for Monitoring the Health and Performance of Converters
Li, Y., Bohara, B., Krishnamoorthy, H. S., & Seshadrinath, J. (2024) · International Telecommunications Energy Conference (INTELEC) · doi:10.1109/intelec60315.2024.10679029 · Cited by 8
An online prediction method of three-dimensional machining residual stress field based on IncepU-net
Wang, Y., Zhao, Z., Ding, W., et al. (2024) · Measurement · doi:10.1016/j.measurement.2024.115794 · Cited by 7
Towards Explainable Visual Vessel Recognition Using Fine-Grained Classification and Image Retrieval
Karus, H., Schwenker, F., Münz, M., & Teutsch, M. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00013 · Cited by 7
An overview of electroencephalogram based human-computer interface
Hasan, M. M., Aris, S. A. M., & Sulaiman, N. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-29150-0.00018-4 · Cited by 7
Precision in Safety: YOLOv9 vs. YOLOv10 for Helmet Image Detection
A’la, F. Y., Firdaus, N., Hartatik, H., et al. (2024) · doi:10.1109/ivit62102.2024.10692595 · Cited by 7
Graph-Based Multi-Feature Fusion Method for Speech Emotion Recognition
Liu, X., Jie, L., & Wang, C. (2024) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s021800142450023x · Cited by 7
Automated Recognition of Snow-Covered and Icy Road Surfaces Based on T-Net of Mount Tianshan
Liu, J., Zhang, Y., Liu, J., Wang, Z., & Zhang, Z. (2024) · Remote Sensing · doi:10.3390/rs16193727 · Cited by 9
Smart PV Monitoring and Maintenance: A Vision Transformer Approach within Urban 4.0
Bounabi, M., Azmi, R., Chenal, J., Diop, E. B., Abdem, S. A. E., Adraoui, M., Hlal, M., & Serbouti, I. (2024) · Technologies · doi:10.3390/technologies12100192 · Cited by 9
Advancing UAV Sensor Fault Diagnosis Based on Prior Knowledge and Graph Convolutional Network
Li, H., Chen, C., Wan, T., Sun, S., Li, Y., & Deng, Z. (2024) · Machines · doi:10.3390/machines12100716 · Cited by 7
Applications of artificial intelligence and machine learning in image processing
Xu, P., Wang, J., Jiang, Y., & Gong, X. (2024) · Frontiers in Materials · doi:10.3389/fmats.2024.1431179 · Cited by 15
Leveraging survival analysis in cost-aware deepnet for efficient hard drive failure prediction
Ahmed, J., & Green, R. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10479-6 · Cited by 8
Two-Stage Multimodal Method for Predicting Intramuscular Fat in Pigs
Liu, W., Liu, T., Zhang, J., & Wang, F. (2024) · Agriculture · doi:10.3390/agriculture14101843 · Cited by 7
YOLO-ESL: An Enhanced Pedestrian Recognition Network Based on YOLO
Wang, F., Yang, X., & Wei, J. (2024) · Applied Sciences · doi:10.3390/app14209588 · Cited by 10
Sun, W., Hu, Y., Zhang, X., et al. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3480156 · Cited by 7
Kumar, M. R., & Vani, B. G. (2024) · doi:10.1109/iccigst60741.2024.10717560 · Cited by 7
Instance segmentation models for detecting floating macroplastic debris from river surface images
Kataoka, T., Yoshida, T., & Yamamoto, N. (2024) · Frontiers in Earth Science · doi:10.3389/feart.2024.1427132 · Cited by 7
Genghuan, 刘. L., Xiangjin, 曾. Z., Dou, J., Zhenbo, 任. R., Liyun, 钟. Z., Jianglei, 邸. D., & Yuwen, 秦. Q. (2024) · Infrared and Laser Engineering · doi:10.3788/irla20240253 · Cited by 7
Liu, W., Han, S., & Rong, N. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109519 · Cited by 7
Wang, Q., Sun, H., He, Q., et al. (2024) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13367 · Cited by 7
The Scope and Limits of Fine-Grained Image and Category Information in the Ventral Visual Pathway
Badwal, M. W., Bergmann, J., Roth, J., Doeller, C. F., & Hebart, M. N. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.0936-24.2024 · Cited by 9
Advancing Pulmonary Nodule Detection with ARSGNet: EfficientNet and Transformer Synergy
Oumlaz, M., Oumlaz, Y., Oukaira, A., et al. (2024) · Electronics · doi:10.3390/electronics13224369 · Cited by 7
Hopfield and Hinton’s neural network revolution and the future of AI
Wang, J. Z., & Wyble, B. (2024) · Patterns · doi:10.1016/j.patter.2024.101094 · Cited by 9
Chu, Z., Geng, J., Yang, Q., Yi, X., & Dong, W. (2024) · Aerospace · doi:10.3390/aerospace11110930 · Cited by 7
Distance and Angle Insensitive Radar-Based Multi-Human Posture Recognition Using Deep Learning
Abdullah, S., Ahmed, S., Choi, C. U., et al. (2024) · Sensors · doi:10.3390/s24227250 · Cited by 7
Spatial weighting — An effective incorporation of geological expertise into deep learning models
Wang, W., Zhao, C., & Wu, Y. (2024) · Geochemistry · doi:10.1016/j.chemer.2024.126212 · Cited by 8
Yao, M., Cheng, D., Zhang, L., Liu, L., Song, S. J., Wu, H., & Song, A. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112444 · Cited by 8
Learning domain-adaptive palmprint anti-spoofing feature from multi-source domains
Liu, C., Shao, H., & Zhong, D. (2024) · Displays · doi:10.1016/j.displa.2024.102871 · Cited by 7
Mohammadzadeh, M., Kremer, G. E. O., Olafsson, S., & Kremer, P. A. (2024) · Automation · doi:10.3390/automation5040033 · Cited by 8
Geological remote sensing interpretation via a local-to-global sensitive feature fusion network
Wang, S., Huang, X., Han, W., et al. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.104258 · Cited by 7
A salient feature establishment tactic for cassava disease recognition
Zhang, J., Zhang, B., Chen, Z., Nyalala, I., Chen, K., & Gao, J. (2024) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2024.11.004 · Cited by 7
Enhancing Binary Convolutional Neural Networks for Hyperspectral Image Classification
Tang, X., Zhang, K., Zhou, X., Zeng, L., & Huang, S. (2024) · Remote Sensing · doi:10.3390/rs16234398 · Cited by 8
Multi-Factor Prediction of Water Inflow from the Working Face Based on an Improved SSA-RG-MHA Model
Ding, Y., Yin, S., Dai, Z., Lian, H., & Bu, C. (2024) · Water · doi:10.3390/w16233390 · Cited by 7
ResNest-SVM-based method for identifying single-phase ground faults in active distribution networks
Lian, Q., Luo, X., Lin, D., Lin, C. E., Chen, B., & Guo, Z. (2024) · Frontiers in Energy Research · doi:10.3389/fenrg.2024.1501737 · Cited by 8
Xiao, Z.-G., Chen, X., Zhang, D., et al. (2024) · World Journal of Gastroenterology · doi:10.3748/wjg.v30.i48.5111 · Cited by 7
Ai, D., & Mo, F. (2024) · Journal of Intelligent Material Systems and Structures · doi:10.1177/1045389x241291214 · Cited by 8
Yue, N., Li, L., Caini, M., et al. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.112364 · Cited by 7
An X-ray image-based pruned dense convolution neural network for tuberculosis detection
Too, E. C., Mwathi, D. G., Gitonga, L. K., et al. (2024) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2024.100169 · Cited by 7
Ares-Flash: Efficient Parallel Integer Arithmetic Operations Using NAND Flash Memory
Chen, J., Gao, C., Lu, Y., Zhang, Y., & Shu, J. (2024) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1109/micro61859.2024.00109 · Cited by 7
Improving Computer Vision Interpretability: Transparent Two-Level Classification for Complex Scenes
Scholz, S., Weidmann, N. B., STEINERT-THRELKELD, Z., Keremoğlu, E., & Goldlüecke, B. (2024) · Political Analysis · doi:10.1017/pan.2024.18 · Cited by 9
Qian, C., Marques, J. A. L., & Fong, S. J. (2024) · International Conference on Big Data and Internet of Things (BDIOT) · doi:10.1145/3697355.3697382 · Cited by 7
Analyze and Improve Differentially Private Federated Learning: A Model Robustness Perspective
Zhang, S., Huang, J., & Li, P. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3518058 · Cited by 8
TrashNeXt: Classification of recyclable water pollutants using deep transfer learning method
Tanvir, J., Mehedi, S. T., Paul, B. K., & Morshed, M. (2024) · Case Studies in Chemical and Environmental Engineering · doi:10.1016/j.cscee.2024.101073 · Cited by 9
ConvBiFuseNet: a parallel fusion model with routing attention for MRI brain tumor classification
Liu, S., Wei, D., Zhang, J., Ji, X., & Cao, H. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06758-7 · Cited by 7
Sharma, S., Guleria, K., & Dogra, A. (2025) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/0115734056333970241212132150 · Cited by 7
An innovative approach to parasite classification in biomedical imaging using neural networks
Aytaç, Ö., Şenol, F. F., Tuncer, I., et al. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110014 · Cited by 7
Duvall, J., & Duraisamy, K. (2025) · Data-Centric Engineering · doi:10.1017/dce.2024.21 · Cited by 7
Zhang, Y., Zhao, Y., Wang, M., Dong, Y., Yang, B., Gong, Y., & Feng, X. (2025) · Displays · doi:10.1016/j.displa.2025.102971 · Cited by 7
Automated detection of sheep eye temperature using thermal images and improved YOLOv7
Zhang, Y., Liu, G., & Wang, J. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.109925 · Cited by 7
DDC: Dynamic distribution calibration for few-shot learning under multi-scale representation
Chen, L., Gu, Y., Guo, Y., Dong, F., Jiang, D., & Chen, Y. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113030 · Cited by 7
Iranipour, B., Sadeghian, M., & Mohajerani, E. (2025) · Materials Today Communications · doi:10.1016/j.mtcomm.2025.111625 · Cited by 8
Ren, D., Li, M., Hong, Z., Liu, L., Huang, J., Sun, H., Ren, S., Sao, P., Wang, W., & Zhang, J. (2025) · Ecological Indicators · doi:10.1016/j.ecolind.2025.113073 · Cited by 7
Yin, J. Y., Ma, R. Q., & Ge, S. (2025) · Frontiers of Engineering Management · doi:10.1007/s42524-025-4030-5 · Cited by 8
Song, H., Zhao, Q., & Dong, H. (2025) · Electric Power Systems Research · doi:10.1016/j.epsr.2025.111435 · Cited by 7
Intelligent recognition of steel plate surface defect based on deep convolutional GAN
Jiang, B., Zhou, P., Sun, X., & Chai, T. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11007-w · Cited by 8
Hameed, A., Shah, S. K., Khan, S. U., et al. (2025) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70050 · Cited by 7
State-of-the-art review and benchmarking of barcode localization methods
Vezzali, E., Bolelli, F., Santi, S., & Grana, C. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110259 · Cited by 9
Nunnari, G., & Calvari, S. (2025) · Applied Sciences · doi:10.3390/app15052604 · Cited by 10
Qawasmeh, B., Oh, J.-S., & Kwigizile, V. (2025) · Applied Sciences · doi:10.3390/app15062928 · Cited by 13
Aircraft Target Detection in Remote Sensing Images Based on Improved YOLOv7-Tiny Network
Zhu, R., Jin, H. L., Han, Y., et al. (2025) · IEEE Access · doi:10.1109/access.2025.3551320 · Cited by 7
Deep Learning-Based Semantic Segmentation for Objective Colonoscopy Quality Assessment
Vulpoi, R. A., Ciobanu, A., Drug, V. L., Mihai, C., Bărboi, O., Floria, D., Coseru, A. I., OLTEANU, A. V., Rosca, V., & Luca, M. (2025) · Journal of Imaging · doi:10.3390/jimaging11030084 · Cited by 7
Wang, T., Zhang, X., Zhou, Y., Chen, Y., Zhao, L., Tan, T., & Tong, T. (2025) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2025.3547635 · Cited by 9
Zhu, J., Wu, J., Yang, Z., Jiang, Y. L., & Chen, Y. (2025) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2025.3550786 · Cited by 9
IoT Based Smart Energy Consumption Predictionfor Home Appliances
Rahman, A., Hossain, S., Ahmed, S., et al. (2025) · International Journal of Information Engineering and Electronic Business · doi:10.5815/ijieeb.2025.02.06 · Cited by 7
Battery State of Health Estimation Based on Energy Features and ResNet‐SVR Model
Xiao, Y., Zhen-feng, L., Huang, C., & Yang, F. (2025) · Quality and Reliability Engineering International · doi:10.1002/qre.3771 · Cited by 8
A Survey on Deep Learning-Based Lane Detection Algorithms for Camera and LiDAR
Sun, M.-H., Kong, S.-H., & Paek, D.-H. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3554695 · Cited by 7
“In recent years, active studies on CNN have led to the emergence of new types of deep neural networks, including AlexNet [51], VGGNet [52], GoogLeNet [53], ResNet [54], and others.”
Nasr, M., Brzostowski, K., Obuchowicz, R., El‐Samie, F. E. A., & Piórkowski, A. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105242 · Cited by 7
Attention-based hybrid contrastive learning for unsupervised person re-identification
Qin, W., Li, Y., Zhang, J., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-97818-2 · Cited by 7
Zhang, E., & Zhang, E. (2025) · Frontiers in Environmental Science · doi:10.3389/fenvs.2025.1569621 · Cited by 10
Listwise ranking predictor for evolutionary neural architecture search
Li, N., Ma, L., Wang, R., et al. (2025) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2025.101956 · Cited by 7
Xu, H., Zhang, Z., Ye, H., Song, J., & Chen, Y. (2025) · Electronics · doi:10.3390/electronics14102029 · Cited by 10
MEWC: A user-friendly AI workflow for customised wildlife-image classification
Brook, B. W., Buettel, J. C., van Lunteren, P., Rajmohan, P. P., & Aandahl, Z. (2025) · Peer Community Journal · doi:10.24072/pcjournal.565 · Cited by 12
BA-Net: Bridge attention in deep neural networks
Zhang, R., Zou, R., Zhao, Y., Zhang, Z., Chen, J., Cao, Y., Hu, C., & Song, H. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128525 · Cited by 8
Advanced Deep Lung Care Net: A Next Generation Framework for Lung Cancer Prediction
Saha, N., Mondal, R., Banerjee, A., Debnath, R., & Chatterjee, S. (2025) · doi:10.38124/ijisrt/25jun1801 · Cited by 9
Automatic Classification and Diagnostic Analysis of Microscopic Blood Images Using R CNN Model
Tulaganova, F., Omonov, S., & Xo‘jamqulov, A. (2024) · International Conference on Future Networks & Distributed Systems (ICFNDS) · doi:10.1145/3726122.3726187 · Cited by 7
Deep neural hashing for content-based medical image retrieval: A survey
Manna, A., Sista, R., & Sheet, D. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110547 · Cited by 8
Ameur, M. B., Chebil, J., Habaebi, M. H., & Tahar, J. B. H. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1597981 · Cited by 11
Theory and Practice of Hierarchical Data-driven Descent for Optimal Deformation Estimation
Tian, Y., & Narasimhan, S. G. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0838-5 · Cited by 7
Evaluating visual and textual features for predicting user ‘likes’
Guntuku, S. C., Roy, S., & Weisi, L. (2015) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2015.7177381 · Cited by 7
Prediction gradients for feature extraction and analysis from convolutional neural networks
Lo, H. Z., Cohen, J., & Ding, W. (2015) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2015.7163154 · Cited by 7
Efficient 24/7 object detection in surveillance videos
Feris, R., Bobbitt, R., Pankanti, S., & Sun, M. (2015) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2015.7301791 · Cited by 7
DeepBag: Recognizing Handbag Models
Wang, Y., Li, S., & Kot, A. C. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2480228 · Cited by 7
Hua, X., & Li, J. (2014) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew.2014.6890616 · Cited by 7
Ghodrati, M., Rajaei, K., & Ebrahimpour, R. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00078 · Cited by 7
A Bayesian generative model for learning semantic hierarchies
Mittelman, R., Sun, M., Kuipers, B. J., & Savarese, S. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.00417 · Cited by 7
Carneiro, G., Peng, T., Bayer, C., et al. (2015) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.81 · Cited by 7
Shamma, D. A., Kennedy, L. S., Li, L.-J., Thomée, B., Jin, H., & Yuan, J. (2016) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2818048.2819989 · Cited by 6
Zero Shot Deep Learning from Semantic Attributes
Burlina, P., Schmidt, A., & Wang, I.-J. (2015) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2015.140 · Cited by 7
Scene-driven Retrieval in Edited Videos using Aesthetic and Semantic Deep Features
Baraldi, L., Grana, C., & Cucchiara, R. (2016) · International Conference on Multimedia Retrieval · doi:10.1145/2911996.2912012 · Cited by 9
Zoghbi, S., Heyman, G., Gómez, J. C., & Moens, M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27674-8_35 · 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
