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 41,701–41,800 of 108,439
Crowd Counting Based on Multiscale Spatial Guided Perception Aggregation Network
Chen, Z., Zhang, S., Zheng, X., Zhao, X., & Kong, Y. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3304348 · Cited by 17
On Quantum Hyperparameters Selection in Hybrid Classifiers for Earth Observation Data
Sebastianelli, A., Rosso, M. P. D., Ullo, S. L., & Gamba, P. E. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3308105 · Cited by 21
Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning
Yan, S., Su, Y., Luo, X., et al. (2023) · Remote Sensing · doi:10.3390/rs15174165 · Cited by 16
Strawberry Defect Identification Using Deep Learning Infrared–Visible Image Fusion
Lu, Y., Gong, M., Li, J., & Ma, J. (2023) · Agronomy · doi:10.3390/agronomy13092217 · Cited by 17
Radar specific emitter identification via the Attention-GRU model
Zhang, W., Zhang, F., Zhao, Z., & Liu, F. (2023) · Digital Signal Processing · doi:10.1016/j.dsp.2023.104198 · Cited by 19
Liu, L., Tang, D., Li, X. H., & Ouyang, Y. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16565-6 · Cited by 16
Dataset Distillation Using Parameter Pruning
Li, G., Togo, R., OGAWA, T., et al. (2023) · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · doi:10.1587/transfun.2023eal2053 · Cited by 16
Finamore, A., Wang, C., Krolikowski, J., Navarro, J. M., Chen, F., & Rossi, D. (2023) · ACM Internet Measurement Conference (IMC) · doi:10.1145/3618257.3624820 · Cited by 17
Self-Supervised Adversarial Learning for Domain Adaptation of Pavement Distress Classification
Wu, Y., Hong, M., Li, A., Huang, S., Liu, H., & Ge, Y. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3314680 · Cited by 18
DCVAE-adv: A Universal Adversarial Example Generation Method for White and Black Box Attacks
Xu, L., & Zhai, J. (2023) · Tsinghua Science & Technology · doi:10.26599/tst.2023.9010004 · Cited by 18
Mallikarjunaiah, S. M. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110863 · Cited by 22
Kwon, M.-R., Youn, I., Lee, M. Y., et al. (2023) · Academic Radiology · doi:10.1016/j.acra.2023.09.013 · Cited by 16
Deploying Convolutional Neural Network to reduce waste in production system
Shahin, M., Chen, F. F., Bouzary, H., & Hosseinzadeh, A. (2023) · Manufacturing Letters · doi:10.1016/j.mfglet.2023.08.127 · Cited by 18
Spatial Bias for attention-free non-local neural networks
Go, J., & Ryu, J. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122053 · Cited by 18
Mukherjee, N., & Sengupta, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17254-0 · Cited by 20
Enhancing glomeruli segmentation through cross-species pre-training
Andreini, P., Bonechi, S., & Dimitri, G. M. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126947 · Cited by 17
Resource Allocation of Federated Learning Assisted Mobile Augmented Reality System in the Metaverse
Zhou, X., Li, Y., & Zhao, J. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10279550 · Cited by 16
Breast Cancer Diagnosis Using Lightweight Deep Convolution Neural Network Model
Kausar, T., Lu, Y. H., & Kausar, A. (2023) · IEEE Access · doi:10.1109/access.2023.3326478 · Cited by 20
Zhang, Q., Wang, G., Wang, G., Song, W., Wei, X., & Hu, Y. (2023) · Remote Sensing · doi:10.3390/rs15215121 · Cited by 16
Xiong, H., Li, J., Su, G., et al. (2023) · Journal of Applied Geophysics · doi:10.1016/j.jappgeo.2023.105219 · Cited by 16
Wu, X., Li, L., Zhu, H., Zhou, G., Li, L., Su, F., He, S., Wang, Y., & Long, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122363 · Cited by 17
Li, T., Xu, H., & Sun, H. (2023) · Applied Sciences · doi:10.3390/app132111740 · Cited by 16
Underwater acoustic target recognition using RCRNN and wavelet-auditory feature
Qi, P., Yin, G., & Zhang, L. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17406-2 · Cited by 16
Ang, K. M., Lim, W. H., Tiang, S. S., Sharma, A., Eid, M. M., Tawfeek, S. M., Khafaga, D. S., Alharbi, A. H., & Abdelhamid, A. A. (2023) · Biomimetics · doi:10.3390/biomimetics8070525 · Cited by 18
Liang, G., Zulu, J., Xing, X., & Jacobs, N. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3331438 · Cited by 19
Lee, H., Chatterjee, I., & Cho, G.-S. (2023) · Applied Sciences · doi:10.3390/app132212214 · Cited by 19
Shi, Y., Olsson, L. T., Hoadley, K. A., Calhoun, B. C., Marron, J. S., Geradts, J., Niethammer, M., & Troester, M. A. (2023) · npj Breast Cancer · doi:10.1038/s41523-023-00597-0 · Cited by 20
Surveying the landscape of text summarization with deep learning: A comprehensive review
Wang, G., & Wu, W. (2023) · Discrete Mathematics Algorithms and Applications · doi:10.1142/s1793830923300047 · Cited by 19
FAST: Enhancing Federated Learning Through Adaptive Data Sampling and Local Training
Wang, Z., Xu, H., Xu, Y., Jiang, Z., Liu, J., & Chen, S. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3334398 · Cited by 17
Cecil, R. M., & Sugden, L. A. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010979 · Cited by 19
GuideFuse: A Novel Guided Auto-Encoder Fusion Network for Infrared and Visible Images
Zhang, Z., Li, H., Xu, T., Wu, X., & Fu, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3306537 · Cited by 17
Color Transfer for Images: A Survey
Lv, C., Zhang, D., Geng, S., Wu, Z., & Huang, H. F. (2023) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3635152 · Cited by 23
Kumari, V., Kumar, N., Sampath Kumar, K., Kumar, A., Sanagala, S. S., Saxena, S., Khanna, N. N., Laird, J. R., Singh, N., Fouda, M. M., et al. (2023) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd10120485 · Cited by 20
Shi, C., Ding, M., Wang, L., et al. (2023) · Remote Sensing · doi:10.3390/rs15235620 · Cited by 16
MonoVAN: Visual Attention for Self-Supervised Monocular Depth Estimation
Indyk, I., & Makarov, I. (2023) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar59233.2023.00138 · Cited by 16
Liu, F., Zhang, Z., & Li, S. (2023) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2023.3332075 · Cited by 17
Remove and recover: two stage convolutional autoencoder based sonar image enhancement algorithm
Liu, T., Yan, S., & Wang, G. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17673-z · Cited by 17
On the compression of neural networks using ℓ 0 -norm regularization and weight pruning
Oliveira, F. D. D. R., Batista, E. L. O., & Seara, R. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.12.019 · Cited by 17
Argho, A. G., Maswood, M. M. S., Mahmood, M. I., & Mondol, N. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200315 · Cited by 22
de Miranda, V. L., de Souza, E. P., Bambil, D. R., Khalighifar, A., Peterson, A. T., Nascimento, F. A. D. O., Gurgel‐Gonçalves, R., & Abad‐Franch, F. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102430 · Cited by 17
Bordbar, M., Helfroush, M. S., Danyali, H., et al. (2023) · BioMedical Engineering OnLine · doi:10.1186/s12938-023-01186-9 · Cited by 16
Liu, Z., Du, H., Lao, F., Shen, Z., Lv, Y.-H., Zhou, L., Jiang, L., & Liu, J. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.108552 · Cited by 16
Mapping the tidal marshes of coastal Virginia: a hierarchical transfer learning approach
Lv, Z., Nunez, K., Brewer, E., & Runfola, D. M. (2023) · GIScience & Remote Sensing · doi:10.1080/15481603.2023.2287291 · Cited by 18
Topo-CXR: Chest X-ray TB and Pneumonia Screening with Topological Machine Learning
Ahmed, F., Nuwagira, B., Torlak, F., & Coşkunuzer, B. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00246 · Cited by 15
Evaluation of Cryptocurrency Price Prediction Using LSTM and CNNs Models
Wen, N. S., & Ling, L. S. (2023) · JOIV International Journal on Informatics Visualization · doi:10.30630/joiv.7.3-2.2344 · Cited by 28
A Novel CNN Model for Classification of Chinese Historical Calligraphy Styles in Regular Script Font
Huang, Q. Y., Li, M. M., Agustin, D., Li, L. D., & Jha, M. (2023) · Sensors · doi:10.3390/s24010197 · Cited by 18
Zhao, H. M., Liu, S., Tong, X., Du, Q., Bruzzone, L., Du, K., Zhang, J., & Lu, X. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3348931 · Cited by 18
Potential diagnostic application of a novel deep learning- based approach for COVID-19
Sadeghi, A., Sadeghi, M., Sharifpour, A., Fakhar, M., Zakariaei, Z., Sadeghi, M., Rokni, M., Zakariaei, A., Banimostafavi, E. S., & Hajati, F. (2024) · Scientific Reports · doi:10.1038/s41598-023-50742-9 · Cited by 16
A generic plug & play diffusion-based denosing module for medical image segmentation
Li, G., Jin, D., Zheng, Y., Cui, J., Gai, W., & Qi, M. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106096 · Cited by 18
FedAVE: Adaptive data value evaluation framework for collaborative fairness in federated learning
Wang, Z., Peng, Z., Fan, X., Wang, Z., Wu, S., Yu, R., Yang, P., Zheng, C., & Wang, C. (2024) · Neurocomputing · doi:10.1016/j.neucom.2023.127227 · Cited by 19
Fan, H., Ren, Z., Cao, X., et al. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3351265 · Cited by 16
RFVIR: A robust federated algorithm defending against Byzantine attacks
Wang, Y., Zhai, D., & Xia, Y. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102251 · Cited by 17
Su, S., Jia, X., Zhan, L., et al. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e24097 · Cited by 16
DiffGuard: Semantic Mismatch-Guided Out-of-Distribution Detection using Pre-trained Diffusion Models
Gao, R., Zhao, C., Hong, L., & Xu, Q. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00152 · Cited by 18
HybridAugment++: Unified Frequency Spectra Perturbations for Model Robustness
Yucel, M. K., Cinbiş, R. G., & Duygulu, P. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00526 · Cited by 19
Semi-supervised domain generalization with evolving intermediate domain
Lin, L., Xie, H., Sun, Z., Chen, W., Liu, W., Yu, Y., & Zhang, L. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110280 · Cited by 18
Multiscale Defect Extraction Neural Network for Green Coffee Bean Defects Detection
Chang, S.-J., & Liu, K.-H. (2024) · IEEE Access · doi:10.1109/access.2024.3356596 · Cited by 16
Physics-Informed Machine Learning with Optimization-Based Guarantees: Applications to Ac Power Flow
Jalving, J., Eydenberg, M., Blakely, L., Castillo, A., Kilwein, Z., Skolfield, J. K., Boukouvala, F., & Laird, C. D. (2022) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2023.109741 · Cited by 25
Chang, Y., Liu, C., & You, S. D. (2024) · Information · doi:10.3390/info15020082 · Cited by 16
Wang, M., Xu, W., Cao, G., et al. (2024) · Building Simulation · doi:10.1007/s12273-023-1099-9 · Cited by 16
Incomplete multi-view clustering via local and global bagging of anchor graphs
Li, A., Xu, H., Feng, C., Yang, H., & Xu, S. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123402 · Cited by 19
Foggia, P., Greco, A., Roberto, A., Saggese, A., & Vento, M. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10241-5 · Cited by 20
Dual Hybrid Attention Mechanism-Based U-Net for Building Segmentation in Remote Sensing Images
Lei, J., Liu, X., Yang, H., et al. (2024) · Applied Sciences · doi:10.3390/app14031293 · Cited by 16
Du, C., Lin, C., Jin, R., Chai, B., Yao, Y., & Su, S. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17983-2 · Cited by 21
Pencil: Private and Extensible Collaborative Learning without the Non-Colluding Assumption
Liu, X., Liu, Z., Li, Q., Xu, K., & Xu, M. (2024) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.24512 · Cited by 16
Abad, G., Ersoy, O., Picek, S., & Urbieta, A. (2024) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.24334 · Cited by 24
Zhukov, A., Rivero, A., Benois‐Pineau, J., Zemmari, A., & Mosbah, M. (2024) · Sensors · doi:10.3390/s24041171 · Cited by 18
Jamil, A., Rehman, S. U., Mahmood, K., Villar, M. G., Prola, T. A., Díez, I. D. L. T., Samad, M. A., & Ashraf, I. (2024) · IEEE Access · doi:10.1109/access.2024.3365528 · Cited by 23
RetractedmXception and dynamic image for hand gesture recognition
Karsh, B., Laskar, R. H., & Karsh, R. K. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09509-0 · Cited by 16
A comprehensive review of deep learning-based variant calling methods
Junjun, R., Zhengqian, Z., Ying, W., Jialiang, W., & Liu, Y. (2024) · Briefings in Functional Genomics · doi:10.1093/bfgp/elae003 · Cited by 20
Wang, B., Zhao, H., Wang, X., Lyu, G., Chen, K., Xu, J., Cui, G., Zhong, L., Yu, L., Huang, H., et al. (2024) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2024.02.002 · Cited by 16
Tian, M., Yao, Z., Zhou, Y., Gan, Q., Wang, L., Lü, H., Wang, S., Zhou, P., Dai, Z., Zhang, S., et al. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-023-04838-5 · Cited by 19
Overlapping representations of observed actions and action‐related features
Kabulska, Z., Zhuang, T., & Lingnau, A. (2024) · Human Brain Mapping · doi:10.1002/hbm.26605 · Cited by 17
Optimal azimuth angle selection for limited SAR vehicle target recognition
Zhang, L., Leng, X., Feng, S., Ma, X., Ji, K., Kuang, G., & Liu, L. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.103707 · Cited by 22
Multidimensional morphological analysis of live sperm based on multiple-target tracking
Yang, H. Y., Ma, M., Chen, X., Chen, G., Shen, Y., Zhao, L., Wang, J., Yan, F., Huang, D., Gao, H., et al. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.02.025 · Cited by 20
Towards a universal mechanism for successful deep learning
Meir, Y., Tzach, Y., Hodassman, S., Tevet, O., & Kanter, I. (2024) · Scientific Reports · doi:10.1038/s41598-024-56609-x · Cited by 19
Fast style transfer for ethnic patterns innovation
Zheng, Y., Jiao, J., Ye, F., Zhou, Y., & Li, W. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123627 · Cited by 21
A two-parameter extended logistic chaotic map for modern image cryptosystems
Latoui, A., & Daachi, M. E. H. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104463 · Cited by 18
Zhu, L., Yin, W., Yang, Y., Wu, F., Zeng, Z., Gu, Q., Wang, X., Zhou, C., & Ye, N. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02036-4 · Cited by 16
Deep learning architecture for sparse and noisy turbulent flow data
Sofos, F. D., Drikakis, D., & Kokkinakis, I. W. (2024) · Physics of Fluids · doi:10.1063/5.0200167 · Cited by 17
Altabey, W. A. (2024) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2024.108277 · Cited by 16
SCSONet: spatial-channel synergistic optimization net for skin lesion segmentation
Chen, H., Li, Z., Huang, X., et al. (2024) · Frontiers in Physics · doi:10.3389/fphy.2024.1388364 · Cited by 16
On the Role of Server Momentum in Federated Learning
Sun, J., Wu, X., Huang, H., & Zhang, A. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i13.29439 · Cited by 23
Bi-ViT: Pushing the Limit of Vision Transformer Quantization
Li, Y., Xu, S. Y., Lin, M., Cao, X., Liu, C., Sun, X., & Zhang, B. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i4.28109 · Cited by 22
Generalized latent multi-view clustering with tensorized bipartite graph
Zhang, D., Huang, H., Zhao, Q., & Zhou, G. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106282 · Cited by 15
Super-resolution reconstruction of LF NMR image based on DDPM for fruit non-destructive testing
Yang, Y., Yang, K., Zeng, S., Wang, L., Li, B., & Yang, Z. (2024) · Journal of Food Composition and Analysis · doi:10.1016/j.jfca.2024.106219 · Cited by 16
Deep Segmentation Techniques for Breast Cancer Diagnosis
Schutte, S., & Uddin, J. (2024) · BioMedInformatics · doi:10.3390/biomedinformatics4020052 · Cited by 17
AI and augmented reality for 3D Indian dance pose reconstruction cultural revival
Jayanthi, J. G., & Maheswari, P. U. (2024) · Scientific Reports · doi:10.1038/s41598-024-58680-w · Cited by 16
Anatomically aware dual-hop learning for pulmonary embolism detection in CT pulmonary angiograms
Condrea, F., Rapaka, S., Lucian Mihai, I. T. U., Sharma, P. K., Sperl, J. I., Ali, A. M., & Leordeanu, M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108464 · Cited by 20
Bogucki, A., John, I., Zinkiewicz, Ł., et al. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2024.1363190 · Cited by 16
Analysis of Pothole Detection Accuracy of Selected Object Detection Models Under Adverse Conditions
Frnda, J., Bandyopadhyay, S., Pavličko, M., et al. (2024) · Transport and Telecommunication Journal · doi:10.2478/ttj-2024-0016 · Cited by 16
FEASTA: A Flexible and Efficient Accelerator for Sparse Tensor Algebra in Machine Learning
Zhong, K., Zhu, Z., Dai, G., Wang, H., Yang, X., Zhang, H., Si, J., Mao, Q., Zeng, S., Hong, K., et al. (2024) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3620666.3651336 · Cited by 18
Aeroengine Blades Damage Detection and Measurement Based on Multimodality Fusion Learning
Wu, X., Wei, X., Xu, H., Hou, Y., Li, C., Yin, Y., He, W., & Zhou, L. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3394505 · Cited by 17
Estimation of wind pressure field on low-rise buildings based on a novel conditional neural network
Wang, H., Bocchini, P., & Padgett, J. E. (2024) · Journal of Wind Engineering and Industrial Aerodynamics · doi:10.1016/j.jweia.2024.105752 · Cited by 16
AUTOMATION IN ANALYTICAL CHEMISTRY: THE ROLE OF AI IN CHROMATOGRAPHY
Divekar, K., & Prasad, S. K. (2024) · International Journal of Applied Pharmaceutics · doi:10.22159/ijap.2024v16i3.50290 · Cited by 21
SIAlex: Species identification and monitoring based on bird sound features
Duan, L., Yang, L., & Guo, Y. X. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102637 · Cited by 16
EvCap: Element-Aware Video Captioning
Liu, S., Li, A., Zhao, Y., Wang, J., & Wang, Y. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3399933 · Cited by 20
Farjon, G., & Edan, Y. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.108988 · Cited by 16
LGNet: Local and global point dependency network for 3D object detection
Ma, J., Huang, Y., Qian, C., Kang, J. S., Liu, J., Zhang, H., & Hong, W. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110585 · Cited by 17
A Novel Deep Learning Approach for Myocardial Infarction Detection and Multi-Label Classification
Abbas, S., Ojo, S., Krichen, M., Alamro, M. A., Mihoub, A., & Vilčeková, L. (2024) · IEEE Access · doi:10.1109/access.2024.3401744 · Cited by 19
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
