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 33,701–33,800 of 108,435
Learning FRAME Models Using CNN FiltersPreprint
Lu, Y., Zhu, S., & Wu, Y. N. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.08379 · Cited by 24
Ma, X., Guo, F.-M., Niu, W., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.05073 · Cited by 24
Bhowmik, N., Wang, Q., Gaus, Y. F. A., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.11508 · Cited by 24
Song, Z., Zou, S., Zhou, W., Huang, Y., Shao, L., Yuan, J., Gou, X., Jin, W., Wang, Z., Chen, X., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.30.927749 · Cited by 24
Boullé, N., Nakatsukasa, Y., & Townsend, A. (2020) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.17863/cam.92936 · Cited by 36
Improved Residual Networks for Image and Video RecognitionPreprint
Duţă, I. C., Liu, L., Zhu, F., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.04989 · Cited by 24
Defining Benchmarks for Continual Few-Shot LearningPreprint
Antoniou, A., Patacchiola, M., Ochal, M., & Storkey, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.11967 · Cited by 43
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A ReviewPreprint
Li, Y., Ma, L., Zhong, Z., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.09830 · Cited by 24
Data Movement Is All You Need: A Case Study on Optimizing Transformers
Ivanov, A., Dryden, N., Ben-Nun, T., Li, S., & Hoefler, T. (2020) · arXiv · doi:10.48550/arxiv.2007.00072 · Cited by 78
Wu, Y., Dong, H., Grosse, R., & Ba, J. L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.04212 · Cited by 72
A Survey on Machine Learning from Few SamplesPreprint
Lu, J., Gong, P., Ye, J., Zhang, J., & Zhang, C. (2020) · arXiv (Cornell University) · doi:10.4230/oasics.dx.2025.10 · Cited by 24
A Generalized Neural Tangent Kernel Analysis for Two-layer Neural NetworksPreprint
Chen, Z., Cao, Y. J., Gu, Q., & Zhang, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.04026 · Cited by 39
Dos and Don'ts of Machine Learning in Computer SecurityPreprint
Arp, D., Quiring, E., Pendlebury, F., Warnecke, A., Pierazzi, F., Wressnegger, C., Cavallaro, L., & Rieck, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.09470 · Cited by 222
Masuda, T., Ragoza, M., & Koes, D. R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.14442 · Cited by 56
Xiang, L., Wang, W., Hu, X., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.12885 · Cited by 24
Understanding the Behaviour of Contrastive LossPreprint
Wang, F., & Liu, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.09740 · Cited by 22
Genome-wide prediction of DNA mutation effect on nucleosome positions for yeast synthetic genomicsPreprint
Routhier, E., Pierre, E., Khodabandelou, G., et al. (2020) · Genome Research · doi:10.1101/gr.264416.120 · Cited by 24
WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face RecognitionPreprint
Zhu, Z. D., Huang, G., Deng, J., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.04098 · Cited by 24
Distribution Alignment: A Unified Framework for Long-tail Visual RecognitionPreprint
Zhang, S., Li, Z., Yan, S., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.16370 · Cited by 24
Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamicsPreprint
Choksi, B., Mozafari, M., O’May, C. B., Ador, B., Alamia, A., & VanRullen, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.02749 · Cited by 28
TVT: Transferable Vision Transformer for Unsupervised Domain AdaptationPreprint
Yang, J., Liu, J., Xu, N., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.05988 · Cited by 24
Relating Graph Neural Networks to Structural Causal ModelsPreprint
Zečević, M., Dhami, D. S., Veličković, P., & Kersting, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.04173 · Cited by 70
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel SizesPreprint
Romero, D. W., Bruintjes, R.-J., Tomczak, J. M., Bekkers, E. J., Hoogendoorn, M., & van Gemert, J. C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.08059 · Cited by 72
Assaying Out-Of-Distribution Generalization in Transfer LearningPreprint
Wenzel, F., Dittadi, A., Gehler, P., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2207.09239 · Cited by 24
Video-based Human Action Recognition using Deep Learning: A ReviewPreprint
Pham, H., Khoudour, L., Crouzil, A., Zegers, P., & Velastín, S. A. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2208.03775 · Cited by 45
A Comprehensive Review of Metaheuristics for Hyperparameter Optimization in Machine Learning
Narayanan, R. T., & Ganesh, N. (2024) · Wiley eBooks · doi:10.1002/9781394233953.ch2 · Cited by 32
StuffNet: Using ‘Stuff’ to Improve Object Detection
Brahmbhatt, S., Christensen, H. I., & Hays, J. (2017) · 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2017.109 · Cited by 24
Convolutional Neural Networks for the Identification of African Lions from Individual Vocalizations
Trapanotto, M., Nanni, L., Brahnam, S., & Guo, X. (2022) · Journal of Imaging · doi:10.3390/jimaging8040096 · Cited by 24
Rare Genetic Variants in LDLR , APOB , and PCSK9 Are Associated With Aortic Stenosis
Rämö, J. T., Jurgens, S. J., Kany, S., Choi, S. H., Wang, X., Smirnov, A. N., Friedman, S. F., Maddah, M., Khurshid, S., Ellinor, P. T., et al. (2024) · Circulation · doi:10.1161/circulationaha.124.070982 · Cited by 26
Lesort, T., Lomonaco, V., Stoian, A., Maltoni, D., Filliat, D., & Díaz-Rodríguez, N. (2019) · Information Fusion · doi:10.1016/j.inffus.2019.12.004 · Cited by 594
MDLdroid: a ChainSGD-reduce Approach to Mobile Deep Learning for Personal Mobile Sensing
Zhang, Y., Gu, T., & Zhang, X. (2020) · 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1109/ipsn48710.2020.00-45 · Cited by 23
A survey machine learning based object detections in an image
Mohmmad, S., Ramesh, D., Kothandaraman, D., Sudarshan, E. C. G., Pasha, S. N., & Shaik, M. A. (2022) · AIP conference proceedings · doi:10.1063/5.0081892 · Cited by 23
Multimodal metadata assignment for cultural heritage artifacts
Rei, L., Mladenić, D., Dorozynski, M., Rottensteiner, F., Schleider, T., Raphaël Troncy, A., Lozano, J. S., & Gaitán, M. (2022) · Multimedia Systems · doi:10.1007/s00530-022-01025-2 · Cited by 34
RF Fingerprinting Needs Attention: Multi-task Approach for Real-World WiFi and Bluetooth
Jagannath, A., Kane, Z., & Jagannath, J. (2022) · Globecom · doi:10.1109/globecom48099.2022.10001572 · Cited by 26
EEAGER: A Neural Network Model for Finding Beaver Complexes in Satellite and Aerial Imagery
Fairfax, E., Zhu, E., Clinton, N., et al. (2023) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2022jg007196 · Cited by 23
Gearbox Fault Diagnosis Based on Gramian Angular Field and CSKD-ResNeXt
Liu, Y., Dou, S., Du, Y., & Wang, Z. (2023) · Electronics · doi:10.3390/electronics12112475 · Cited by 23
Hierarchical Curriculum Learning for No-Reference Image Quality Assessment
Wang, J., Chen, Z., Yuan, C., Li, B., Ma, W., & Hu, W. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01851-5 · Cited by 27
Prado, E. M. G., Filho, C. R. D. S., & Carranza, E. J. M. (2023) · Economic Geology · doi:10.5382/econgeo.5023 · Cited by 24
Yang, W., Zhao, Y., Li, Q., Zhu, F., & Su, Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121151 · Cited by 26
SwinDAE: Electrocardiogram Quality Assessment Using 1D Swin Transformer and Denoising AutoEncoder
Chen, G., Shi, T., Xie, B., et al. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3314698 · Cited by 23
CNN-Transformer: A deep learning method for automatically identifying learning engagement
Xiong, Y., Xinya, G., & Xu, J. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-12058-z · Cited by 23
Toy, T. (2023) · AI & Society · doi:10.1007/s00146-023-01786-y · Cited by 26
Amieghemen, G. E., & Sherif, M. M. (2023) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2023.2263441 · Cited by 24
Bit-Balance: Model-Hardware Codesign for Accelerating NNs by Exploiting Bit-Level Sparsity
Sun, W., Zou, Z., Liu, D., Sun, W., Chen, S., & Kang, Y. (2023) · IEEE Transactions on Computers · doi:10.1109/tc.2023.3324477 · Cited by 25
A method for real-time optimal heliostat aiming strategy generation via deep learning
Wu, S., & Ni, D. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107279 · Cited by 23
Understanding and Mitigating Dimensional Collapse in Federated Learning
Shi, Y., Liang, J., Zhang, W., Xue, C., Tan, V. Y. F., & Bai, S. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3338063 · Cited by 24
Nagarajan, A., Robinson, N., Ang, K. K., Chua, K. S. G., Chew, E., & Guan, C. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad152f · Cited by 26
Advances and Challenges in Semantic Communications: A Systematic Review
Zhang, P., Liu, Y., Song, Y., & Zhang, J. (2023) · National Science Open · doi:10.1360/nso/20230029 · Cited by 31
What's next? Forecasting scientific research trends
Ofer, D., Kaufman, H., & Linial, M. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e23781 · Cited by 35
Rawat, S., Bisht, B., Bisht, V., Rawat, N., & Rawat, A. S. (2023) · Franklin Open · doi:10.1016/j.fraope.2023.100069 · Cited by 24
LPST-Det: Local-Perception-Enhanced Swin Transformer for SAR Ship Detection
Yang, Z., Xia, X., Liu, Y., Wen, G., Zhang, W. E., & Guo, L. (2024) · Remote Sensing · doi:10.3390/rs16030483 · Cited by 29
A Multiscale Attention Mechanism Super-Resolution Confocal Microscopy for Wafer Defect Detection
Sun, X.-F., Zhang, B., Wang, Y., Mai, J., Wang, Y., Tan, J., & Wang, W. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3358693 · Cited by 25
Ullah, N., Khan, J. A., Almakdi, S., Alshehri, M. S., Qathrady, M. A., Anwar, M. S., & Syed, I. (2024) · Biochemistry and Cell Biology · doi:10.1139/bcb-2023-0265 · Cited by 23
Multi-task deep learning for large-scale buildings energy management
Wang, R., Rayhana, R., Gholami, M., Herrera, O. E., Liu, Z., & Mérida, W. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.113964 · Cited by 25
Wave Height and Period Estimation from X-Band Marine Radar Images Using Convolutional Neural Network
Zuo, S., Wang, D., Wang, X. R., et al. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12020311 · Cited by 23
Hansen, V., Jensen, J., Kusk, M. W., Gerke, O., Tromborg, H. B., & Lysdahlgaard, S. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111399 · Cited by 29
STCGRU: A hybrid model based on CNN and BiGRU for mild cognitive impairment diagnosis
Zhou, H., Yin, L., Su, R., Zhang, Y., Yuan, Y., Xie, P., & Li, X. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108123 · Cited by 27
Li, X., Gao, Y., Chang, H., Huang, D., Ma, Y., Pan, R., Qi, H., Wang, F., Wu, S., Xu, K., et al. (2024) · Statistical Theory and Related Fields · doi:10.1080/24754269.2024.2343151 · Cited by 25
Aligning Geometric Spatial Layout in Cross-View Geo-Localization via Feature Recombination
Zhang, Q., & Zhu, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i7.28554 · Cited by 32
A Comprehensive Survey on Backdoor Attacks and Their Defenses in Face Recognition Systems
Roux, Q. L., Bourbao, E., Teglia, Y., & Kallas, K. (2024) · IEEE Access · doi:10.1109/access.2024.3382584 · Cited by 30
CNN based plant disease identification using PYNQ FPGA
Perumal, V. K., Supriyaa, T., Santhosh, P., et al. (2024) · Systems and Soft Computing · doi:10.1016/j.sasc.2024.200088 · Cited by 23
Prediction model of measurement errors in current transformers based on deep learning.
Li, Z.-H., Cui, J.-X., Lu, H., Zhou, F., Diao, Y.-L., & Li, Z.-X. (2024) · Review of Scientific Instruments · doi:10.1063/5.0190206 · Cited by 25
Material augmented semantic segmentation of point clouds for building elements
Liang, H., Yeoh, J. K. W., & Chua, D. K. H. (2024) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13198 · Cited by 23
Dynamic heterogeneous federated learning with multi-level prototypes
Guo, S., Wang, H., & Geng, X. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110542 · Cited by 23
Convolutional neural network-based wind pressure prediction on low-rise buildings
Huang, Y., Wu, H., Fu, J., et al. (2024) · Engineering Structures · doi:10.1016/j.engstruct.2024.118078 · Cited by 24
Improved weight initialization for deep and narrow feedforward neural network
Lee, H.-W., Kim, Y., Yang, S. Y., & Choi, H. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106362 · Cited by 29
Liu, G. S., Jovanovic, N., Sung, C. K., & Doyle, P. C. (2024) · Otolaryngology · doi:10.1002/ohn.809 · Cited by 33
Deep learning for optical tweezers
Ciarlo, A., Ciriza, D. B., Selin, M., Maragò, O. M., Sasso, A., Pesce, G., Volpe, G., & Goksör, M. (2024) · Nanophotonics · doi:10.1515/nanoph-2024-0013 · Cited by 27
Impact of early visual experience on later usage of color cues
Vogelsang, M., Vogelsang, L., Gupta, P., Gandhi, T. K., Shah, P., Swami, P., Gilad‐Gutnick, S., Ben-Ami, S., DIAMOND, S. P., Ganesh, S., et al. (2024) · Science · doi:10.1126/science.adk9587 · Cited by 27
Jin, K., Zhang, J., Wang, Z., Zhang, J., Liu, N., Li, M., & Ma, Z. (2024) · Agricultural Water Management · doi:10.1016/j.agwat.2024.108901 · Cited by 23
Ma, L., Luo, X., Hong, H., Meng, F., & Wu, Q. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3407661 · Cited by 26
Hu, B., Liu, J., Zhao, R., et al. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad5861 · Cited by 23
Singh, I. P., Ghorbel, E., Oyedotun, O. K., & Aouada, D. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104062 · Cited by 25
Abdullahi, M., Oyelade, O. N., Kana, A. F. D., Bagiwa, M. A., Abdullahi, F. B., Junaidu, S. B., Iliyasu, I., Ore-Ofe, A., & Chiroma, H. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19823-3 · Cited by 31
Lightweight citrus leaf disease detection model based on ARMS and cross-domain dynamic attention
Mo, H., & Wei, L. (2024) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2024.102133 · Cited by 25
Liu, H., Zhang, Y., & Chen, Y. (2024) · Symmetry · doi:10.3390/sym16080952 · Cited by 25
New Trends in Ovarian Cancer Diagnosis Using Deep Learning: A Systematic Review
El-Khatib, M., Popescu, D., Teodor, O. M., & Ichim, L. (2024) · IEEE Access · doi:10.1109/access.2024.3434722 · Cited by 24
Chau, M. T. (2024) · Journal of medical imaging and radiation sciences · doi:10.1016/j.jmir.2024.101733 · Cited by 31
Dai, K., Shao, J., Gong, B., Jing, L., & Chen, Y. (2024) · Aquacultural Engineering · doi:10.1016/j.aquaeng.2024.102460 · Cited by 24
Community-engaged artificial intelligence research: A scoping review
Loftus, T. J., Balch, J. A., Abbott, K. L., Hu, D., Ruppert, M. M., Shickel, B., Ozrazgat‐Baslanti, T., Efron, P. A., Tighe, P. J., Hogan, W. R., et al. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000561 · Cited by 27
Frontiers and developments of data augmentation for image: From unlearnable to learnable
Gan, L., Jiang, J., Bai, J., Su, Y., Su, Z., & Liu, H. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102660 · Cited by 30
Three Pillars Improving Vision Foundation Model Distillation for Lidar
Puy, G., Gidaris, S., Boulch, A., Siméoni, O., Sautier, C., Pérez, P., Bursuc, A., & Marlet, R. (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/cvpr52733.2024.02033 · Cited by 37
On Cyber-Social Learning: A Critique of Artificial Intelligence in Education
Cope, B., & Kalantzis, M. (2024) · Postdigital science and education · doi:10.1007/978-3-031-64487-0_1 · Cited by 31
Research progress in deep learning for ceramics surface defect detection
Zhou, M. Y., Wu, T., Xia, Z., He, B., Kong, L. B., & Su, H. (2024) · Measurement · doi:10.1016/j.measurement.2024.115956 · Cited by 26
Liu, Z., Zhong, Y., Ma, G., Wang, X., & Zhang, L. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3483072 · Cited by 33
A review of recent advances in 3D Gaussian Splatting for optimization and reconstruction
Luo, J., Huang, T., Wang, W., & Wei, F. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105304 · Cited by 30
Mizuno, Y., HOSOI, A., Koshita, H., Tsunoda, D., & Kawada, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-75884-2 · Cited by 22
Singh, S. A., Choudhari, S. J., & Desai, K. A. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102906 · Cited by 23
A Multisensor Feature Fusion Attention Convolutional Neural Network for Complex Magnetic Leakage
Lang, X., & Wang, Z. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3493872 · Cited by 23
Multi-Scale Feature Fusion Enhancement for Underwater Object Detection
Xiao, Z., Li, Z., Li, H., et al. (2024) · Sensors · doi:10.3390/s24227201 · Cited by 23
Li, X., Zhao, M., Zhong, S., Li, J., Fu, S., & Yan, Z. (2024) · Energy · doi:10.1016/j.energy.2024.134030 · Cited by 25
Explainable screening of oral cancer via deep learning and case-based reasoning
Cimino, M. G. C. A., Campisi, G., Galatolo, F. A., Neri, P., Tozzo, P., Parola, M., Mantia, G. L., & Fede, O. D. (2025) · Smart Health · doi:10.1016/j.smhl.2024.100538 · Cited by 28
Point Tree Transformer for Point Cloud Registration
Wang, M., Chen, G., Yang, Y. M., Yuan, L., & Yue, Y. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3526680 · Cited by 28
Lu, Y. H., & Sun, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-85488-z · Cited by 30
A Feature-Enhanced Small Object Detection Algorithm Based on Attention Mechanism
Quan, Z., & Sun, J. (2025) · Sensors · doi:10.3390/s25020589 · Cited by 29
Guo, Z., Ma, R., Ma, H., et al. (2025) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2024.3525115 · Cited by 23
FSENet: Feature suppression and enhancement network for tiny object detection
Hu, H., Chen, S.-B., You, Z., & Tang, J. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111425 · Cited by 31
Progress of Complex System Process Analysis Based on Modern Spectroscopy Combined With Chemometrics
Li, M., Cai, Q., Zhang, T., Tang, H., & Li, H. (2025) · Journal of Chemometrics · doi:10.1002/cem.70006 · Cited by 28
Zhou, Y., Rashid, F. ‘. N., Daud, M. M., Hasan, M. K., & Chen, W. (2025) · Sensors · doi:10.3390/s25051586 · Cited by 31
Jiang, R., Zheng, X., Sun, J., Chen, L., Xu, G., & Zhang, R. (2025) · Cognitive Neurodynamics · doi:10.1007/s11571-025-10232-2 · Cited by 25
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
