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 36,701–36,800 of 108,435
Degree-of-Node Task Scheduling of Fine-Grained Parallel Programs on Heterogeneous Systems
Han, L., Li, M., Jia, C., et al. (2019) · Journal of Computer Science and Technology · doi:10.1007/s11390-019-1962-4 · Cited by 20
An end-to-end learning method for industrial defect detection
Wu, Y., Guo, D., Liu, H., & Huang, Y. (2019) · Assembly Automation · doi:10.1108/aa-08-2018-114 · Cited by 20
Classification of Degraded Traffic Signs Using Flexible Mixture Model and Transfer Learning
Mannan, A., Javed, K., Rehman, A. U., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2947069 · Cited by 20
DeepChunk: Deep Q-Learning for Chunk-Based Caching in Wireless Data Processing Networks
Wang, Y., Li, Y., Lan, T., et al. (2019) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2019.2947550 · Cited by 20
HPDL: Towards a General Framework for High-performance Distributed Deep Learning
Li, D., Lai, Z., Ge, K., Zhang, Y., Zhang, Z., Wang, Q., & Wang, H. (2019) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00173 · Cited by 19
Liu, Q., Feng, C., Song, Z., Louis, J., & Zhou, J. H. (2019) · Applied Sciences · doi:10.3390/app9224871 · Cited by 25
Structure-functional analysis and synthesis of deep convolutional neural networks
Vizilter, Y. V., Сергеевич, Г. В., & Zheltov, S. Y. (2019) · Computer Optics · doi:10.18287/2412-6179-2019-43-5-886-900 · Cited by 20
Wang, H., Han, Y., Chen, Z., et al. (2019) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab59a4 · Cited by 20
And the Bit Goes Down: Revisiting the Quantization of Neural Networks
Stock, P., Joulin, A., Gribonval, R., Graham, B., & Jeǵou, H. (2020) · HAL · Cited by 21
Glioma Grading Prediction by Exploring Radiomics and Deep Learning Features
Xiao, T., Hua, W., Li, C., et al. (2019) · doi:10.1145/3364836.3364877 · Cited by 20
Barycenters of Natural Images - Constrained Wasserstein Barycenters for Image Morphing
Simon, D., & Aberdam, A. (2020) · 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/cvpr42600.2020.00793 · Cited by 20
Chen, A., Zhu, L., Zang, H., Ding, Z., & Zhan, S. (2019) · Journal of Management Science and Engineering · doi:10.1016/j.jmse.2020.01.002 · Cited by 20
Backdoor Suppression in Neural Networks using Input Fuzzing and Majority Voting
Sarkar, E., Alkindi, Y., & Maniatakos, M. (2020) · IEEE Design and Test · doi:10.1109/mdat.2020.2968275 · Cited by 20
Does an LSTM forget more than a CNN? An empirical study of catastrophic forgetting in NLP
Arora, G. S., Rahimi, A., & Baldwin, T. (2019) · Cited by 18
Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification
Zhang, Z., Sun, Y., Zhang, Z., et al. (2019) · Proceedings · doi:10.1109/icdm.2019.00094 · Cited by 20
A Redundant Fault-tolerant Aviation Control System Based on Deep Neural Network
Zhang, Y., Tao, L., Wang, S., et al. (2019) · IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT) · doi:10.1109/iccasit48058.2019.8973215 · Cited by 20
Assay Type Detection Using Advanced Machine Learning Algorithms
Tania, M. H., Lwin, K. T., Shabut, A. M., Abuhassan, K., Kaiser, M. S., & Hossain, M. A. (2019) · International Conference on Software, Knowledge, Information Management and Applications (SKIMA) · doi:10.1109/skima47702.2019.8982449 · Cited by 20
A real-time recognition method of static gesture based on DSSD
Zhang, Y., Zhou, W., Wang, Y., et al. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08725-9 · Cited by 20
Enabling Efficient Fast Convolution Algorithms on GPUs via MegaKernels
Jia, L., Liang, Y. E., Li, X., Lu, L., & Yan, S. (2020) · IEEE Transactions on Computers · doi:10.1109/tc.2020.2973144 · Cited by 20
Copiaco, A., Ritz, C. H., Fasciani, S., et al. (2019) · IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) · doi:10.1109/isspit47144.2019.9001814 · Cited by 20
Wang, J., Cui, Y., Shi, G., Zhao, J., Yang, X., Qiang, Y., Du, Q., Ma, Y., & Kazihise, N. G. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01658-8 · Cited by 21
Investigating the Use of Deep Neural Networks for Software Defect Prediction
Samir, M., El‐Ramly, M., & Kamel, A. A. (2019) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa47632.2019.9035240 · Cited by 20
Animal Species Recognition Using Deep Learning
Ibraheam, M., Gebali, F., Li, K. F., & Sielecki, L. E. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-44041-1_47 · Cited by 21
Motion boundary emphasised optical flow method for human action recognition
Peng, C., Huang, H., Tsoi, A. C., Lo, S., Liu, Y., & Yang, Z. (2020) · IET Computer Vision · doi:10.1049/iet-cvi.2018.5556 · Cited by 20
Pulmonary nodule detection using hybrid two‐stage 3D CNNs
Tan, M., Wu, F., Yang, B., Ma, J., Kong, D., Chen, Z., & Long, D. (2020) · Medical Physics · doi:10.1002/mp.14161 · Cited by 20
Xu, Z., Chen, Y., Yang, F., et al. (2020) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi9040238 · Cited by 20
Toward automatic comparison of visualization techniques: Application to graph visualization
Giovannangeli, L., Bourqui, R., Giot, R., & Auber, D. (2020) · Visual Informatics · doi:10.1016/j.visinf.2020.04.002 · Cited by 20
Periocular Biometrics for non-ideal images: with off-the-shelf Deep CNN & Transfer Learning approach
Kumari, P., & Seeja, K. R. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.03.234 · Cited by 20
A Distributed Computing Real-Time Safety System of Collaborative Robot
Gradolewski, D., Masłowski, D., Dziak, D., et al. (2020) · Elektronika ir Elektrotechnika · doi:10.5755/j01.eie.26.2.25757 · Cited by 20
Zhai, Y., Deng, W., Lan, T., Sun, B. S., Ying, Z., Gan, J., Mai, C., Li, J., Labati, R. D., Piuri, V., et al. (2020) · Remote Sensing · doi:10.3390/rs12091385 · Cited by 20
Comparative Study of Movie Shot Classification Based on Semantic Segmentation
Bak, H.-Y., & Park, S.-B. (2020) · Applied Sciences · doi:10.3390/app10103390 · Cited by 20
Multi-Atlas Brain Parcellation Using Squeeze-and-Excitation Fully Convolutional Networks
Tang, Z., Liu, X., Li, Y., Yap, P., & Shen, D. G. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2994445 · Cited by 20
Wei, W., McElroy, C., & Dey, S. (2020) · IEEE Access · doi:10.1109/access.2020.2997341 · Cited by 20
Integrating Image-Based and Knowledge-Based Representation Learning
Xie, R., Heinrich, S., Liu, Z., Weber, C., Yao, Y., Wermter, S., & Sun, M. (2019) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2019.2906685 · Cited by 24
An Improved Fuzziness based Random Vector Functional Link Network for Liver Disease Detection
Cao, W., Yang, P., Ming, Z., Cai, S., & Zhang, J. (2020) · IEEE International Conference on Big Data Security on Cloud, IEEE International Conference on High Performance and Smart Computing, and IEEE International Conference on Intelligent Data and Security (BigDataSecurity/HPSC/IDS) · doi:10.1109/bigdatasecurity-hpsc-ids49724.2020.00019 · Cited by 22
Object Recognition with Hybrid Deep Learning Methods and Testing on Embedded Systems
Taşpınar, Y. S., & Selek, M. (2020) · International Journal of Intelligent Systems and Applications in Engineering · doi:10.18201/ijisae.2020261587 · Cited by 20
Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets
Choe, J., Oh, S. J., Chun, S., Lee, S., Akata, Z., & Shim, H. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3169881 · Cited by 28
Zhong, Y., Ye, R., Liu, T., Hu, Z., & Zhang, L. (2020) · Journal of Geophysical Research Space Physics · doi:10.1029/2019ja027590 · Cited by 21
Potato Bud Detection with Improved Faster R-CNN
Xi, R., Hou, J., & Lou, W. (2020) · Transactions of the ASABE · doi:10.13031/trans.13628 · Cited by 20
Huang, S., He, C., & Cheng, R. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3187384 · Cited by 20
Informedia@TRECVID 2014 MED and MER
Yu, S.-I., Jiang, L., Xu, Z., Lan, Z., Xu, S., Chang, X., & Martin, L. J. (2014) · Maryland Shared Open Access Repository (USMAI Consortium) · doi:10.13016/m2pcms-bkos · Cited by 23
Data augmentation for bearing fault detection with a light weight CNN
Oh, J. W., & Jeong, J. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.07.013 · Cited by 20
Su, H., Pan, H., Lu, C., et al. (2020) · Sensors · doi:10.3390/s20164409 · Cited by 20
Irina: Accelerating DNN Inference with Efficient Online Scheduling
Wu, X., Xu, H. L., & Wang, Y. (2020) · Asia-Pacific Workshop on Networking (APNet) · doi:10.1145/3411029.3411035 · Cited by 24
Künstliche Intelligenz in der Bildanalyse – Grundlagen und neue Entwicklungen
Pouly, M., Koller, T., Gottfrois, P., & Lionetti, S. (2020) · Der Hautarzt · doi:10.1007/s00105-020-04663-7 · Cited by 22
Optimizing FFT-Based Convolution on ARMv8 Multi-core CPUs
Wang, Q., Li, D., Huang, X., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-57675-2_16 · Cited by 20
Exploring Convolution Neural Network for Branch Prediction
Mao, Y., Zhou, H., Gui, X., & Shen, J. (2020) · IEEE Access · doi:10.1109/access.2020.3017196 · Cited by 22
Vanishing Point Detection and Rail Segmentation Based on Deep Multi-Task Learning
Li, X., Zhu, L., Yu, Z., Guo, B., & Wan, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3019318 · Cited by 23
Detection and recognition of cursive text from video frames
Mirza, A., Zeshan, O., Atif, M., & Siddiqi, I. (2020) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-020-00523-5 · Cited by 22
Hsiao, C., Lin, T., Lin, C., et al. (2020) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc44109.2020.9176226 · Cited by 20
Boutillon, A., Borotikar, B. S., Burdin, V., & Conze, P.-H. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102364 · Cited by 20
Structure Aware Generative Adversarial Networks for Hyperspectral Image Classification
Alipour-Fard, T., & Arefi, H. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3022781 · Cited by 20
Naik, N., & Mehta, M. A. (2020) · IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · doi:10.1109/conecct50063.2020.9198376 · Cited by 20
Aplicaciones de las redes neuronales y el deep learning a la ingeniería biomédica
Sarmiento-Ramos, J. L. (2020) · Revista UIS Ingenierías · doi:10.18273/revuin.v19n4-2020001 · Cited by 29
Performance Evaluation of Deception Against Synthetic Aperture Radar Based on Multifeature Fusion
Tian, T., Zhou, F., Li, Y., et al. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3028858 · Cited by 20
Privacy-Preserving Machine Learning Using Federated Learning and Secure Aggregation
Lia, D., & Togan, M. (2020) · International Conference on Electronics, Computers and Artificial Intelligence (ECAI) · doi:10.1109/ecai50035.2020.9223127 · Cited by 22
DD-Net: spectral imaging from a monochromatic dispersed and diffused snapshot
Hauser, J., Zeligman, A., Averbuch, A., et al. (2020) · Applied Optics · doi:10.1364/ao.404524 · Cited by 19
Querying little is enough: Model inversion attack via latent information
Mo, K., Liu, X., Huang, T., et al. (2020) · International Journal of Intelligent Systems · doi:10.1002/int.22315 · Cited by 20
Pre-trained Data Augmentation for Text Classification
Abonizio, H., & Barbon, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61377-8_38 · Cited by 23
Single-Shot Face Anti-Spoofing for Dual Pixel Camera
Wu, X., Zhou, J., Liu, J., et al. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.3035879 · Cited by 20
Siddiqi, U. F., Sait, S. M., & Uysal, M. (2020) · IEEE Access · doi:10.1109/access.2020.3038923 · Cited by 20
Inference guided feature generation for generalized zero-shot learning
Han, Z., Fu, Z., Li, G., & Yang, J. J. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.10.080 · Cited by 20
Thiery, S., Abdine, M. Z. E., Heger, J., et al. (2020) · International Journal of Material Forming · doi:10.1007/s12289-020-01598-1 · Cited by 20
Hierarchically Decoupled Spatial-Temporal Contrast for Self-supervised Video Representation Learning
Zhang, Z., & Crandall, D. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00105 · Cited by 23
Self-supervised Learning of Orc-Bert Augmentor for Recognizing Few-Shot Oracle Characters
Han, W.-H., Ren, X., Lin, H., Fu, Y., & Xue, X. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69544-6_39 · Cited by 22
Otolith identification using a deep hierarchical classification model
Stock, M., Nguyen, B., Courtens, W., Verstraete, H., Stienen, E. W. M., & Bernard De Baets, C. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105883 · Cited by 22
A Semi-Supervised Deep Rule-Based Approach for Complex Satellite Sensor Image Analysis
Gu, X., Angelov, P. P., Zhang, C., et al. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3048268 · Cited by 20
Lee, K. W., & Chin, R. K. Y. (2020) · IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) · doi:10.1109/iicaiet49801.2020.9257859 · Cited by 20
Wei, H., Shen, H., Li, J., Zhao, R., & Chen, Z. (2020) · Optics & Laser Technology · doi:10.1016/j.optlastec.2020.106773 · Cited by 21
Joint Data Collection and Resource Allocation for Distributed Machine Learning at the Edge
Chen, M., Wang, H., Meng, Z., Xu, H., Xu, Y., Liu, J., & Huang, H. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.3045436 · Cited by 20
Greedy auto-augmentation for n-shot learning using deep neural networks
Naghizadeh, A., Metaxas, D., & Liu, D. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.11.015 · Cited by 20
Open-World Class Discovery with Kernel Networks
Wang, Z., Salehi, B., Gritsenko, A., Chowdhury, K. R., Ioannidis, S., & Dy, J. (2020) · Proceedings · doi:10.1109/icdm50108.2020.00072 · Cited by 22
Breast cancer mitotic cell detection using cascade convolutional neural network with U-Net
Lu, X., You, Z., Sun, M., et al. (2020) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2021036 · Cited by 20
Spectrogram-Based Classification Of Spoken Foul Language Using Deep CNN
Wazir, A. S. B., Karim, H. B. A., Abdullah, M. H. L., Mansor, S. B., AlDahoul, N., Fauzi, M. F. A., & See, J. (2020) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp48831.2020.9287133 · Cited by 20
Automatically detecting bregma and lambda points in rodent skull anatomy images
Zhou, P., Liu, Z., Wu, H., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0244378 · Cited by 20
Yamaguchi, M., Iwamoto, G., Nishimura, Y., Tamukoh, H., & Morie, T. (2020) · IEEE Access · doi:10.1109/access.2020.3047619 · Cited by 22
Lee, J.-J., & Li, P. (2020) · IEEE International Conference on Computer Design: VLSI in Computers and Processors (ICCD) · doi:10.1109/iccd50377.2020.00027 · Cited by 21
Application of Transfer Learning for Fruits and Vegetable Quality Assessment
Turaev, S., Almisreb, A. A., & Saleh, M. A. (2020) · International Conference on Innovations in Information Technology · doi:10.1109/iit50501.2020.9299048 · Cited by 21
A Review on Deep Learning Based Image Classification of Plant Diseases
Muthukumarana, P. S., & Aponso, A. C. (2020) · International Journal of Computer Theory and Engineering · doi:10.7763/ijcte.2020.v12.1275 · Cited by 20
Li, J., Wang, P., Zhou, Y., Liang, H., & Luan, K. (2021) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.620257 · Cited by 19
Ienaga, N., Higuchi, K., Takashi, T., Gen, K., Tsuda, K., & Terayama, K. (2021) · Scientific Reports · doi:10.1038/s41598-020-80001-0 · Cited by 20
Research on detection algorithm of lithium battery surface defects based on embedded machine vision
Chen, Y., Shu, Y., Li, X., Xiong, C., Cao, S., Wen, X., & Xie, Z. (2021) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-189693 · Cited by 20
Multi-class blind steganalysis using deep residual networks
Singhal, A., & Bedi, P. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10353-2 · Cited by 19
Within‐farm wheat yield forecasting incorporating off‐farm information
Mario Fajardo, P., & Whelan, B. M. (2021) · Precision Agriculture · doi:10.1007/s11119-020-09779-3 · Cited by 20
A rapid screening classifier for diagnosing COVID-19
Xia, Y., Chen, W., Ren, H., Zhao, J., Wang, L., Jin, R., Zhou, J.-S., Wang, Q., Yan, F., Zhang, B., et al. (2021) · International Journal of Biological Sciences · doi:10.7150/ijbs.53982 · Cited by 22
Syed, S., Ehsan, L., Shrivastava, A., Sengupta, S., Khan, M. N., Kowsari, K., Guleria, S., Sali, R., Kant, K., Kang, S., et al. (2021) · Journal of Pediatric Gastroenterology and Nutrition · doi:10.1097/mpg.0000000000003057 · Cited by 19
Interpretability in neural networks towards universal consistency
Monte-Serrat, D. M., & Cattani, C. (2021) · International Journal of Cognitive Computing in Engineering · doi:10.1016/j.ijcce.2021.01.002 · Cited by 20
Real-Time Driving Distraction Recognition Through a Wrist-Mounted Accelerometer
Xie, Z., Li, L., & Xu, X. (2021) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720821995000 · Cited by 21
Improving Unimodal Object Recognition with Multimodal Contrastive Learning
Meyer, J., Eitel, A., Brox, T., & Burgard, W. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341029 · Cited by 22
Self-attention mechanism in person re-identification models
Chen, W., Lu, Y., Ma, H., et al. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10494-4 · Cited by 20
Zhang, Y., Gan, F., & Chen, X. (2020) · IEEE Conference on Energy Internet and Energy System Integration (EI2) · doi:10.1109/ei250167.2020.9346704 · Cited by 22
Fully Automated DCNN-Based Thermal Images Annotation Using Neural Network Pretrained on RGB Data
Ligocki, A., Jelínek, A., Žalud, L., & Rahtu, E. (2021) · Sensors · doi:10.3390/s21041552 · Cited by 21
Mel-spectrogram and Deep CNN Based Representation Learning from Bio-Sonar Implementation on UAVs
Tanveer, M. H., Zhu, H., Ahmed, W., Thomas, A., Imran, B. M., & Salman, M. (2021) · International Conference on Computer, Control and Robotics (ICCCR) · doi:10.1109/icccr49711.2021.9349416 · Cited by 23
Adversarial Binary Mutual Learning for Semi-Supervised Deep Hashing
Wang, G., Hu, Q., Yang, Y., Cheng, J., & Hou, Z. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3055834 · Cited by 20
Accurate Differentially Private Deep Learning on the Edge
Han, R., Li, D., Ouyang, J., Liu, C. H., Wang, G., Wu, D. O., & Chen, L. Y. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3064345 · Cited by 23
Compressing CNNs Using Multilevel Filter Pruning for the Edge Nodes of Multimedia Internet of Things
Liu, X., Wu, L., Dai, C., & Chao, H. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3052016 · Cited by 22
Exposing Semantic Segmentation Failures via Maximum Discrepancy Competition
Yan, J., Zhong, Y., Fang, Y., Wang, Z., & Ma, K. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01450-2 · Cited by 22
Explaining Deep Neural Network using Layer-wise Relevance Propagation and Integrated Gradients
Čík, I., Rasamoelina, A. D., Mach, M., & Sinčák, P. (2021) · IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI) · doi:10.1109/sami50585.2021.9378686 · Cited by 21
Moiré-based sub-nano misalignment sensing via deep learning for lithography
Wang, N., Jiang, W., & Zhang, Y. (2021) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2021.106620 · Cited by 20
Tanaka, R., Naoi, M., Chen, Y., Yamamoto, K., Imakita, K., Tsutsumi, N., Shimoda, A., Hiramatsu, D., Kawakata, H., Ishida, T., et al. (2021) · Geophysical Journal International · doi:10.1093/gji/ggab096 · Cited by 20
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
