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 46,601–46,700 of 108,439
Data science methodologies in smart healthcare: a review
Parida, P. K., Dora, L., Swain, M., Agrawal, S., & Panda, R. (2022) · Health and Technology · doi:10.1007/s12553-022-00648-9 · Cited by 13
The Development of Real-Time Mobile Garbage Detection Using Deep Learning
Fathurrahman, H. I. K., Ma’arif, A., & Chin, L.-Y. (2022) · Jurnal Ilmiah Teknik Elektro Komputer dan Informatika · doi:10.26555/jiteki.v7i3.22295 · Cited by 13
Kabisha, M. S., Rahim, K. A., Khaliluzzaman, M., et al. (2022) · International Journal of Intelligent Systems and Applications in Engineering · doi:10.18201/ijisae.2022.273 · Cited by 13
A Survey: Image Classification Models Based on Convolutional Neural Networks
Peng, L., Qiang, B., & Wu, J. (2022) · International Conference on Computer Research and Development · doi:10.1109/iccrd54409.2022.9730565 · Cited by 14
Seasonal Surface Eddy Mixing in the Kuroshio Extension: Estimation and Machine Learning Prediction
Guan, W., Chen, R., Zhang, H., Yi, Y., & Wei, H. (2022) · Journal of Geophysical Research Oceans · doi:10.1029/2021jc017967 · Cited by 16
Fitton, D., Laurens, E., Hongkarnjanakul, N., Schwob, C., & Mezeix, L. (2022) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2022.100740 · Cited by 15
Liao, J., Liu, L., Duan, H., Huang, Y., Zhou, L., Chen, L., & Wang, C. (2022) · JMIR Medical Informatics · doi:10.2196/28880 · Cited by 13
New insights into Human Hematopoietic Stem and Progenitor Cells via Single-Cell Omics
Zhang, Y., Huang, Y., Hu, L., & Cheng, T. (2022) · Stem Cell Reviews and Reports · doi:10.1007/s12015-022-10330-2 · Cited by 15
Novel-view X-ray projection synthesis through geometry-integrated deep learning
Shen, L., Yu, L., Zhao, W., Pauly, J. M., & Xing, L. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102372 · Cited by 13
Traffic Landmark Matching Framework for HD-Map Update: Dataset Training Case Study
Park, Y.-K., Park, H., Woo, Y.-S., Choi, I.-G., & Han, S.-S. (2022) · Electronics · doi:10.3390/electronics11060863 · Cited by 13
Chiang, J., Lie, W., Huang, H., et al. (2022) · Applied Sciences · doi:10.3390/app12063087 · Cited by 13
Xie, J., Wang, L., Webster, P. J., Yang, Y., Sun, J., Wang, S., & Zhou, H. (2022) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-022-00510-6 · Cited by 15
Wang, Q., Wang, C., Wu, H., Zhao, C., Teng, G., Yu, Y., & Zhu, H. (2022) · Remote Sensing · doi:10.3390/rs14081834 · Cited by 14
Business transaction recommendation for discovering potential business partners using deep learning
Lee, D., & Kim, K. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117222 · Cited by 13
Gudigar, A., Raghavendra, U., Samanth, J., Dharmik, C., Gangavarapu, M. R., Nayak, K., Ciaccio, E. J., Tan, R., Molinari, F., & Acharya, U. R. (2022) · Journal of Imaging · doi:10.3390/jimaging8040102 · Cited by 13
Fault Recognition Method Based on Attention Mechanism and the 3D-UNet
Yu, T., Wang, X., Chen, T. J., et al. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/9856669 · Cited by 13
Kang, J., & Wen, H. (2022) · Physiological Measurement · doi:10.1088/1361-6579/ac6aa3 · Cited by 13
He, B., Zhang, Q., Tong, M., & He, C. (2022) · Remote Sensing · doi:10.3390/rs14092177 · Cited by 13
Whole-Brain Modelling: Past, Present, and Future
Griffiths, J. D., Bastiaens, S. P., & Kaboodvand, N. (2021) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-89439-9_13 · Cited by 23
Artificial Intelligence and its Application in Animal Disease Diagnosis
Sharma, N. (2022) · Journal of animal research · doi:10.30954/2277-940x.01.2022.1 · Cited by 18
A weighted wrapper approach to feature selection
Kusy, M., & Zajdel, R. (2021) · International Journal of Applied Mathematics and Computer Science · doi:10.34768/amcs-2021-0047 · Cited by 13
ActiveZero: Mixed Domain Learning for Active Stereovision with Zero Annotation
Liu, I., Yang, E., Tao, J., et al. (2022) · 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/cvpr52688.2022.01269 · Cited by 13
Wang, C., Zhang, R., & Chang, L. (2022) · Remote Sensing · doi:10.3390/rs14092225 · Cited by 13
Gao, T., Zhou, Y., Duan, S., & Hu, X. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108962 · Cited by 15
Real-Time Object Detection using Deep Learning and Open CV
(2019) · International Journal of Innovative Technology and Exploring Engineering · doi:10.35940/ijitee.l1103.10812s19 · Cited by 15
Yu, H., & Li, X. (2018) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/aspdac.2018.8297275 · Cited by 15
Deep-Learning-Based Classification and Diagnosis of Alzheimer's Disease
Janghel, R. R. (2019) · IGI Global eBooks · doi:10.4018/978-1-7998-0414-7.ch076 · Cited by 14
Comparing Data Sources and Architectures for Deep Visual Representation Learning in Semantics
Kiela, D., Verő, A. L., & Clark, S. R. L. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1043 · Cited by 22
Automatic recognition of micronucleus by combining attention mechanism and AlexNet
Wei, W., Hong, T., Chen, W., et al. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01875-w · Cited by 13
A CNN-Based Deep Learning Approach for SSVEP Detection Targeting Binaural Ear-EEG
Israsena, P., & Pan-ngum, S. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.868642 · Cited by 15
Using CNN for solving two-player zero-sum games
Wu, D., & Lisser, A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117545 · Cited by 14
A novel approach for unlabeled samples in radiation source identification
Yang, H., Zhang, H., Wang, H., et al. (2022) · Journal of Systems Engineering and Electronics · doi:10.23919/jsee.2022.000037 · Cited by 13
Automatic Recognition of Faults in Mining Areas Based on Convolutional Neural Network
Zou, G., Liu, H., Ren, K., Deng, B., & Xue, J. (2022) · Energies · doi:10.3390/en15103758 · Cited by 13
Ghaleb, M. S., Moushier, H., Shedeed, H., & Tolba, M. F. (2022) · International journal of intelligent computing and information sciences/International Journal of Intelligent Computing and Information Sciences · doi:10.21608/ijicis.2022.117060.1156 · Cited by 13
Functional connectivity signatures of political ideology
Yang, S. E., Wilson, J. D., Lu, Z., & Cranmer, S. (2022) · PNAS Nexus · doi:10.1093/pnasnexus/pgac066 · Cited by 13
Deep Learning-Based Path Loss Prediction Using Side-View Images in an UMa Environment
Kuno, N., Inomata, M., Sasaki, M., & Yamada, W. (2022) · European Conference on Antennas and Propagation · doi:10.23919/eucap53622.2022.9769422 · Cited by 15
Yang, C. B., Kim, S. H., & Lim, Y. J. (2022) · Clinical Endoscopy · doi:10.5946/ce.2021.229 · Cited by 13
Zhao, L., Niu, R., Li, B., Chen, T., & Wang, Y. (2022) · Remote Sensing · doi:10.3390/rs14112626 · Cited by 14
Yin, S., Xian, G., & Jiang, J. (2022) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11060326 · Cited by 13
Zhong, J., Jin, L., & Wang, R. (2022) · IET Biometrics · doi:10.1049/bme2.12081 · Cited by 13
EECDN: Energy-efficient Cooperative DNN Edge Inference in Wireless Sensor Networks
Chen, L., Yao, M., Wu, Y., et al. (2022) · ACM Transactions on Internet Technology · doi:10.1145/3544969 · Cited by 13
Low complexity template-based watermarking with neural networks and various embedding templates
Dzhanashia, K., & Evsutin, O. O. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.108194 · Cited by 15
Yao, H., Zhang, Y., Jiang, L., et al. (2022) · Sensors · doi:10.3390/s22134769 · Cited by 13
Vetrekar, N., Prabhu, A. K., Naik, A., et al. (2022) · Journal of Food Processing and Preservation · doi:10.1111/jfpp.16882 · Cited by 13
Feature-Based Deep Learning Classification for Pipeline Component Extraction from 3D Point Clouds
Xu, Z., Kang, R., & Li, H. (2022) · Buildings · doi:10.3390/buildings12070968 · Cited by 13
Sarcasm Over Time and Across Platforms: Does the Way We Express Sarcasm Change?
Bouazizi, M., & Ohtsuki, T. O. (2022) · IEEE Access · doi:10.1109/access.2022.3174862 · Cited by 16
Efficient and Secure Deep Learning Inference in Trusted Processor Enabled Edge Clouds
Li, Y., Zeng, D., Gu, L., Chen, Q., Guo, S., Zomaya, A. Y., & Guo, M. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3187772 · Cited by 14
Eliminating CSI Feedback Overhead via Deep Learning-Based Data Hiding
Guo, J., Wen, C.-K., & Jin, S. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3180806 · Cited by 13
Progressive representation recalibration for lightweight super-resolution
Wen, R., Yang, Z., Chen, T., Li, H., & Li, K. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.07.050 · Cited by 14
Augmented Reality Image Quality Assessment Based on Visual Confusion Theory
Duan, H., Guo, L., Sun, W., Min, X., Chen, L., & Zhai, G. (2022) · IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) · doi:10.1109/bmsb55706.2022.9828671 · Cited by 17
Real-Time Image Based Weapon Detection Using YOLO Algorithms
Gali, M., Dhavale, S., & Kumar, S. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-12641-3_15 · Cited by 13
Can We Mitigate Backdoor Attack Using Adversarial Detection Methods?
Jin, K., Zhang, T., Shen, C. Q., Chen, Y., Fan, M. Y., Lin, C., & Liu, T. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3194642 · Cited by 26
Multi-Task Self-Supervised Learning for Vehicle Classification Based on Carrier-Free UWB Radars
Zhu, Y., Chen, S., Li, X., Zhang, S., & Zhu, L. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3196085 · Cited by 13
Ma, J., Hu, Z., Shao, Q., Wang, Y., Zhou, Y., Liu, J., & Liu, S. (2022) · Diversity · doi:10.3390/d14080624 · Cited by 15
Lightweight Compound Scaling Network for Nasopharyngeal Carcinoma Segmentation from MR Images
Liu, Y., Han, G., & Liu, X. (2022) · Sensors · doi:10.3390/s22155875 · Cited by 13
Liu, T., Li, C., Liu, Z., et al. (2022) · Energies · doi:10.3390/en15165818 · Cited by 13
Learning the dynamics of metamaterials from diffracted waves with convolutional neural networks
Zhai, Y., Kwon, H., Choi, Y., Kovacevich, D. A., & Popa, B.-I. (2022) · Communications Materials · doi:10.1038/s43246-022-00276-w · Cited by 15
Perrin, J. E., Jernigan, S. R., Thayer, J. D., et al. (2022) · Robotics · doi:10.3390/robotics11040068 · Cited by 12
Varga, D. (2022) · Journal of Imaging · doi:10.3390/jimaging8080224 · Cited by 13
Objectification of intracochlear electrocochleography using machine learning
Schuerch, K., Wimmer, W., Dalbert, A., Rummel, C., Caversaccio, M. D., Mantokoudis, G., & Weder, S. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.943816 · Cited by 13
Mota, A. M., Clarkson, M. J., Almeida, P. M. D., & Matela, N. (2022) · Journal of Imaging · doi:10.3390/jimaging8090231 · Cited by 15
Information bottleneck and selective noise supervision for zero-shot learning
Zhou, L., Liu, Y., Zhang, P., Bai, X. P., Gu, L., Zhou, J. W., Yao, Y., Harada, T., Zheng, J. Y., & Hancock, E. R. (2022) · Machine Learning · doi:10.1007/s10994-022-06196-7 · Cited by 14
Schuler, J. P. S., Romaní, S., Puig, D., Rashwan, H. A., & Abdel‐Nasser, M. (2022) · Entropy · doi:10.3390/e24091264 · Cited by 13
Ayu-Characterization of healthy aging from neuroimaging data with deep learning and rsfMRI
Borkar, K., Chaturvedi, A., Vinod, P. K. U., & Bapi, R. S. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.940922 · Cited by 14
Dai, Y., Song, W., Li, Y., & Stefano, L. D. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.09.007 · Cited by 14
A Novel Siamese Network for Few/Zero-Shot Handwritten Character Recognition Tasks
Elaraby, N., Barakat, S., & Rezk, A. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.032288 · Cited by 13
Saghi, A., Junjuri, R., Lensu, L. T., et al. (2022) · Optics Continuum · doi:10.1364/optcon.469753 · Cited by 13
Style Transfer with Bio-realistic Appearance Manipulation for Skin-tone Inclusive rPPG
Ba, Y., Wang, Z., Karinca, D., Bozkurt, O. D., & Kadambi, A. (2022) · IEEE International Conference on Computational Photography (ICCP) · doi:10.1109/iccp54855.2022.9887649 · Cited by 19
Luo, Y., Li, W., Ma, X., et al. (2022) · Information · doi:10.3390/info13100446 · Cited by 13
Computer aided detection of tuberculosis using two classifiers
Ibrahim, A. U., Al-Turjman, F. M., Özsöz, M., & Serte, S. (2022) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt-2021-0310 · Cited by 13
A Study on the Optimal Artificial Intelligence Model for Determination of Urolithiasis
Eun, S.-J., Yun, M. S., Whangbo, T. K., & Kim, K. H. (2022) · International Neurourology Journal · doi:10.5213/inj.2244202.101 · Cited by 14
Generating gender-ambiguous voices for privacy-preserving speech recognition
Stoidis, D., & Cavallaro, A. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-11322 · Cited by 14
Wagatsuma, N., Hidaka, A., & Tamura, H. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.979258 · Cited by 13
Patel, M. J., Kothari, A. M., & Koringa, H. P. (2022) · RADIOELECTRONIC AND COMPUTER SYSTEMS · doi:10.32620/reks.2022.3.12 · Cited by 13
Wang, H., Zhong, J., Xu, Y., Luo, G., Jiang, B., Hu, Q., Lin, Y., & Ran, J. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101868 · Cited by 15
Li, H., Wang, J., Du, X., Hu, Z., & Yang, S. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103106 · Cited by 16
Semi-supervised underwater acoustic source localization based on residual convolutional autoencoder
Jin, P., Wang, B., Li, L., Chao, P., & Xie, F. (2022) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-022-00941-9 · Cited by 13
Cross-domain learning for underwater image enhancement
Li, F., Zheng, J., Zhang, Y., et al. (2022) · Signal Processing Image Communication · doi:10.1016/j.image.2022.116890 · Cited by 13
Gravel road classification based on loose gravel using transfer learning
Saeed, N., Nyberg, R. G., & Alam, M. (2022) · International Journal of Pavement Engineering · doi:10.1080/10298436.2022.2138879 · Cited by 17
Yao, H., Xia, S., & Liu, H. (2022) · Pacific-Basin Finance Journal · doi:10.1016/j.pacfin.2022.101886 · Cited by 15
Yu, S., Li, Y., Xuan, Z., et al. (2022) · Applied Sciences · doi:10.3390/app122111130 · Cited by 13
Quek, T. C., Takahashi, K., Kang, H. G., Thakur, S., Deshmukh, M., Tseng, R. M. W. W., Nguyen, H., Tham, Y. C., Rim, T. H., Kim, S. S., et al. (2022) · The EPMA Journal · doi:10.1007/s13167-022-00301-5 · Cited by 13
Research on Defect Detection in Automated Fiber Placement Processes Based on a Multi-Scale Detector
Zhang, Y., Wang, W., Liu, Q., et al. (2022) · Electronics · doi:10.3390/electronics11223757 · Cited by 13
Spatio-Temporal Network for Sea Fog Forecasting
Park, J., Lee, Y. J., Jo, Y., Kim, J., Han, J. H., Kim, K. J., Kim, Y. T., & Kim, S. B. (2022) · Sustainability · doi:10.3390/su142316163 · Cited by 14
Ran, X., Xue, L., Sang, X., et al. (2022) · Mathematics · doi:10.3390/math10244677 · Cited by 13
Shao, A., Jin, K., Li, Y., Lou, L., Zhou, W., & Ye, J. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.1032144 · Cited by 13
Defect detection algorithm for fabric based on deformable convolutional network
Luo, X., Cheng, Z., Ni, Q., et al. (2022) · Textile Research Journal · doi:10.1177/00405175221143742 · Cited by 13
SRODNet: Object Detection Network Based on Super Resolution for Autonomous Vehicles
Musunuri, Y. R., Kwon, O., & Kung, S. (2022) · Remote Sensing · doi:10.3390/rs14246270 · Cited by 13
TCDNet: Tree Crown Detection From UAV Optical Images Using Uncertainty-Aware One-Stage Network
Wu, W., Fan, X., Qu, H., et al. (2022) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2022.3214281 · Cited by 13
Pham, T. D., Ravi, V., Fan, C., Luo, B., & Sun, X. (2022) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2022.3229561 · Cited by 14
Zhu, G., Liu, X., Tang, S., & Niu, J. (2022) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs54860.2022.00083 · Cited by 17
Complex Video Action Reasoning via Learnable Markov Logic Network
Jin, Y., Zhu, L., & Mu, Y. (2022) · 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/cvpr52688.2022.00324 · Cited by 13
White Blood Cells Image Classification Based on Radiomics and Deep Learning
Wu, W., Liao, S., & Lu, Z. (2022) · IEEE Access · doi:10.1109/access.2022.3223669 · Cited by 13
LSDNet: A Lightweight Self-Attentional Distillation Network for Visual Place Recognition
Peng, G., Huang, Y., Li, H., et al. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9982272 · Cited by 13
Homepage2Vec: Language-Agnostic Website Embedding and Classification
Lugeon, S., Piccardi, T., & West, R. (2022) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v16i1.19380 · Cited by 16
Solar Flare Forecasting with Deep Learning-based Time Series Classifiers
Ji, A., Wen, J., Angryk, R. A., & Aydin, B. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956097 · Cited by 14
DHT: Deformable Hybrid Transformer for Aerial Image Segmentation
Zhang, Y., Gao, X., Duan, Q., Yuan, L., & Gao, X. (2022) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2022.3222916 · Cited by 13
Gao, S. L., Dai, Y., Xu, Y., Chen, J., & Liu, Y. (2023) · Transactions of the Institute of Measurement and Control · doi:10.1177/01423312221140940 · Cited by 14
Qin, Y., Liu, Y., Jiang, X., Li, Y., Xu, H., Shi, Y., & Huo, Z. (2023) · Atmosphere · doi:10.3390/atmos14010145 · Cited by 15
CNN based Approach for Sign Recognition in the Indian Sign language
Unkule, P., Shinde, C., Saurkar, P., et al. (2022) · International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) · doi:10.1109/icaiss55157.2022.10010911 · Cited by 13
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
