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 54,501–54,600 of 108,439
Image Ordinal Classification and Understanding: Grid Dropout with Masking Label
Zhang, C., Zhu, C., Xiao, J., Xu, X., & Liu, Y. (2018) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2018.8486469 · Cited by 8
Liu, B., Liu, Q., Zhang, T., & Yang, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.07.024 · Cited by 9
Feng, Q., & Jarvis, J. F. (2017) · International Cosmic Ray Conference · doi:10.22323/1.301.0826 · Cited by 9
A Neurally-Guided, Parallel Theorem Prover
Rawson, M., & Reger, G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29007-8_3 · Cited by 9
Deep Hashing: A Joint Approach for Image Signature Learning
Mu, Y., & Liu, Z. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10858 · Cited by 11
He, R., Lin, C., & McAuley, J. (2016) · doi:10.1145/2872518.2890534 · Cited by 9
Convolutional Neural Networks for blind decoding in Sparse Code Multiple Access
Abidi, I., Hizem, M., Ahriz, I., et al. (2019) · doi:10.1109/iwcmc.2019.8766707 · Cited by 9
Generative Cooperative Net for Image Generation and Data Augmentation
Xu, Q., Qin, Z., & Wan, T. R. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-14815-7_24 · Cited by 9
Learning Compositional Visual Concepts with Mutual Consistency
Gong, Y., Karanam, S., Wu, Z., Peng, K., Ernst, J., & Doerschuk, P. C. (2018) · 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/cvpr.2018.00903 · Cited by 12
A novel scene text detection algorithm based on convolutional neural network
Ren, X., Chen, K., Yang, X., et al. (2016) · Visual Communications and Image Processing · doi:10.1109/vcip.2016.7805444 · Cited by 9
Learning deep features for online person tracking using non-overlapping cameras: A survey
Narayan, N., Sankaran, N., Setlur, S., & Govindaraju, V. (2019) · Image and Vision Computing · doi:10.1016/j.imavis.2019.07.007 · Cited by 9
Detecting Social Signals in User-Shared Images for Connection Discovery Using Deep Learning
Cheung, M., & She, J. P. M. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2930043 · Cited by 9
Elastic Neural Networks for Classification
Zhou, Y., Bai, Y., Bhattacharyya, S. S., et al. (2019) · IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) · doi:10.1109/aicas.2019.8771475 · Cited by 9
Noguchi, T., Uchiyama, F., Kawata, Y., Machitori, A., Shida, Y., Okafuji, T., Yokoyama, K., Inaba, Y., & Tajima, T. (2019) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.mp.2019-0063 · Cited by 10
A Targeted Data Extraction System for Mobile Devices
Aggarwal, S., Dorai, G., Karabiyik, Ü., Mukherjee, T., Guerra, N., Hernandez, M., Parsons, J., Rathi, K., Chi, H., Aderibigbe, T., et al. (2019) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-28752-8_5 · Cited by 9
Artificial Intelligent in Healthcare
Meiliana, A., Dewi, N. M., & Wijaya, A. (2019) · The Indonesian Biomedical Journal · doi:10.18585/inabj.v11i2.844 · Cited by 9
Learning semantic dependencies with channel correlation for multi-label classification
Xue, L., Jiang, D., Wang, R., Yang, J., & Hu, M. Q. (2019) · The Visual Computer · doi:10.1007/s00371-019-01731-5 · Cited by 9
Nakano, R. (2019) · Distill · doi:10.23915/distill.00019.4 · Cited by 7
Segmenting localized corrosion from rust-removed metallic surface with deep learning algorithm
Zhang, S., Li, Z., Yang, C., & Zhu, C. (2019) · Journal of Electronic Imaging · doi:10.1117/1.jei.28.4.043019 · Cited by 8
Characterizing the Execution of Deep Neural Networks on Collaborative Robots and Edge Devices
Merck, M. L., Wang, B., Liu, L., Jia, C., Siqueira, A., Huang, Q., Saraha, A., Lim, D., Cao, J., Hadidi, R., et al. (2019) · Practice and Experience in Advanced Research Computing · doi:10.1145/3332186.3333049 · Cited by 9
A Convolutional Neural Network Pruning Method Based On Attention Mechanism
Wang, X. J., Yao, W., & Fu, H. (2019) · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · doi:10.18293/seke2019-147 · Cited by 9
Improving Keypoint Matching Using a Landmark-Based Image Representation
Huang, X., Dai, Z., Chen, W., He, L., & Zhang, H. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794420 · Cited by 9
A novel image super-resolution reconstruction algorithm based on improved GANs and gradient penalty
Liu, S., & Li, X. (2019) · International Journal of Intelligent Computing and Cybernetics · doi:10.1108/ijicc-10-2018-0135 · Cited by 9
LCEval: Learned Composite Metric for Caption Evaluation
Sharif, N., White, L., Bennamoun, M., Liu, W., & Shah, S. A. A. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01206-z · Cited by 8
Enhanced Convolutional Neural Network for Abnormal Event Detection in Video Streams
Bouindour, S., Hu, R., & Snoussi, H. (2019) · IEEE International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) · doi:10.1109/aike.2019.00039 · Cited by 9
Multi-Loss-Aware Channel Pruning of Deep Networks
Hu, Y., Sun, S., Li, J., Zhu, J., Wang, X., & Gu, Q. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8802974 · Cited by 9
Deep Net Tree Structure for Balance of Capacity and Approximation Ability
Chui, C. K., Lin, S.-B., & Zhou, D. (2019) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2019.00046 · Cited by 7
Hierarchical Features Fusion for Image Aesthetics Assessment
Zhang, W., Zhai, G., Yang, X., & Yan, J. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803599 · Cited by 9
Sharan, R. V., & Moir, T. J. (2019) · IEEE International Conference on Signals and Systems (ICSigSys) · doi:10.1109/icsigsys.2019.8811088 · Cited by 9
Improving Adversarial Robustness via Attention and Adversarial Logit Pairing
Li, X., Goodman, D., Liu, J., et al. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.752831 · Cited by 9
Autonomous Choice of Deep Neural Network Parameters by a Modified Generative Adversarial Network
Lu, Y., & Velipasalar, S. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803539 · Cited by 9
Zhao, C., Ding, R., & Key, H. L. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8802931 · Cited by 10
Learning to Drive: End-to-End Off-Road Path Prediction
Holder, C. J., & Breckon, T. P. (2020) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2019.2898970 · Cited by 9
Towards Scalable Video Analytics at the Edge
Stone, T., Stone, N., Jain, P. K., Jiang, Y., Kim, K.-H., & Nelakuditi, S. (2019) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/sahcn.2019.8824876 · Cited by 9
PAI-FCNN: FPGA Based Inference System for Complex CNN Models
Xia, L., Diao, L., Zhao, J., Liang, H., Chen, K., Ding, L., Dou, S., Su, Z., Sun, M., Zhang, J., et al. (2019) · IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP) · doi:10.1109/asap.2019.00-21 · Cited by 9
Zhao, Y., & Du, X. (2019) · Methods · doi:10.1016/j.ymeth.2019.09.008 · Cited by 9
Centroid-position-based autofocusing technique for Raman spectroscopy
Cui, H., & Cui, D. (2019) · Optics Express · doi:10.1364/oe.27.027354 · Cited by 9
Hilbert Vector Convolutional Neural Network: 2D Neural Network on 1D Data
Loka, N. R. B. S., Kavitha, M. S., & Kurita, T. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30487-4_36 · Cited by 8
PolishNet-2d and PolishNet-3d: Deep Learning-Based Workpiece Recognition
Liu, F., & Wang, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2940411 · Cited by 10
Hierarchical Deep Learning Approach for Plant Disease Detection
Cósta, J., Silva, C., & Ribeiro, B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-31321-0_33 · Cited by 10
Hardware-Software Co-design Approach for Deep Learning Inference
Paul, D., Singh, J., & Mathew, J. (2019) · International Conference on Smart Computing and Communications (ICSCC) · doi:10.1109/icscc.2019.8843626 · Cited by 9
A Hybrid AI Tool to Extract Key Performance Indicators from Financial Reports for Benchmarking
Brito, E., Sifa, R., Bauckhage, C., et al. (2019) · ACM Symposium on Document Engineering (DocEng) · doi:10.1145/3342558.3345420 · Cited by 9
Diatom Classification Including Morphological Adaptations Using CNNs
Sánchez, C., Vállez, N., Bueno, G., & Cristóbal, G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-31332-6_28 · Cited by 12
Assembly Defect Detection of Atomizers Based on Machine Vision
Wang, J., Hu, H., Chen, L., & He, C. (2019) · International Conference on Automation, Control and Robotics Engineering (CACRE) · doi:10.1145/3351917.3351925 · Cited by 9
Residual Networks Behave Like Boosting Algorithms
Siu, C. (2019) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2019.00017 · Cited by 9
Deep CNN-based Features for Hand-Drawn Sketch Recognition via Transfer Learning Approach
Hayat, S., She, K., Mateen, M., & Yu, Y. (2019) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2019.0100958 · Cited by 12
Abbas, A., & Chalup, S. K. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852013 · Cited by 11
Seeing the Meaning: Vision Meets Semantics in Solving Pictorial Analogy Problems.
Lu, H., Liu, Q., Ichien, N., Yuille, A., & Holyoak, K. J. (2019) · Cognitive Science · Cited by 10
Khemmar, R., Gouveia, M., Decoux, B., & Ertaud, J.-Y. (2019) · Computer Science Research Notes · doi:10.24132/csrn.2019.2902.2.5 · Cited by 9
Improved Machine Learning using Confidence
TaheriNejad, N., & Jantsch, A. (2019) · doi:10.1109/ccece.2019.8861962 · Cited by 9
Al-Faris, M., Chiverton, J. P., Yang, Y., & Ndzi, D. L. (2019) · Journal of Imaging · doi:10.3390/jimaging5100082 · Cited by 9
Deep Learning as a Tool for Early Cinema Analysis
Bhargav, S., van Noord, N., & Kamps, J. (2019) · Workshop on Structuring and Understanding of Multimedia heritAge Contents (SUMAC) · doi:10.1145/3347317.3357240 · Cited by 11
Global Attention Pyramid Network for Semantic Segmentation
Zhang, N., Li, J., Li, Y., et al. (2019) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2019.8865946 · Cited by 9
Passive browser identification with multi-scale Convolutional Neural Networks
Samizade, S., Shen, C. Q., Si, C., & Guan, X. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.10.028 · Cited by 10
CNN-Based Ternary Classification for Image Steganalysis
Kang, S., Park, H., & Park, J.-I. (2019) · Electronics · doi:10.3390/electronics8111225 · Cited by 11
On the Effectiveness of Deep Representation Learning: The Atrial Fibrillation Case
Gadaleta, M., Rossi, M., Topol, E. J., et al. (2019) · Computer · doi:10.1109/mc.2019.2932716 · Cited by 9
Medical Image Classification: A Comparison of Deep Pre-trained Neural Networks
Alebiosu, D. O., & Muhammad, F. P. (2019) · IEEE Student Conference on Research and Development (SCOReD) · doi:10.1109/scored.2019.8896277 · Cited by 11
Intersected EMG Heatmaps and Deep Learning Based Gesture Recognition
Ke, W., Xing, Y., Caterina, G. D., et al. (2020) · International Conference on Machine Learning and Computing (ICMLC) · doi:10.1145/3383972.3383982 · Cited by 9
Lin, Z., Jia, J., Gao, W., & Huang, F. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-190747 · Cited by 8
Multi-scale classification of single-cell gel electrophoresis assay using deep learning algorithm
Erdamar, A., & Akşahin, M. F. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101672 · Cited by 9
YAMADA, Y., Uchiyama, M., Jobashi, M., Koizumi, T., Tamai, T., Sato, N., Tanabe, J., Kimura, K., Ojima, Y., Murakami, R., et al. (2019) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2019.2951391 · Cited by 9
Comparison of Word Embeddings for Extraction from Medical Records
Dudchenko, A., & Kopanitsa, G. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16224360 · Cited by 10
Offset min-sum Optimization for General Decoding Scheduling: A Deep Learning Approach
Abotabl, A. A., Bae, J. H., & Song, K.-B. (2019) · IEEE Vehicular Technology Conference · doi:10.1109/vtcfall.2019.8891434 · Cited by 7
Zheng, L., Zhang, T., & Yu, X. (2019) · doi:10.1109/icdarw.2019.40099 · Cited by 9
AI-based Pilgrim Detection using Convolutional Neural Networks
Jabra, M. B., Ammar, A., Koubâa, A., Cheikhrouhou, O., & Hamam, H. (2020) · International Conference on Advanced Technologies for Signal and Image Processing · doi:10.1109/atsip49331.2020.9231549 · Cited by 10
Comparison of Artificial Intelligence based approaches to cell function prediction
Padi, S., Manescu, P., Schaub, N. J., Hotaling, N. A., Simon, C. G., Bharti, K., & Bajcsy, P. (2019) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2019.100270 · Cited by 9
Zero-Shot Deep Learning for Media Mining: Person Spotting and Face Clustering in Video Big Data
Abdallah, M. S., Kim, H., Ragab, M., & Hemayed, E. E. (2019) · Electronics · doi:10.3390/electronics8121394 · Cited by 10
Detection and Classification of Network Events in LAN Using CNN
Sun, Y., Esaki, H., & Ochiai, H. (2019) · doi:10.1109/incit.2019.8911924 · Cited by 9
Pedestrian Detection and Behavior Recognition Based on Vision
Zhan, H., Liu, Y., Cui, Z., et al. (2019) · IEEE Intelligent Transportation Systems Conference (ITSC) · doi:10.1109/itsc.2019.8917264 · Cited by 9
Deep Learning Neural Encoders for Motor Cortex
Liang, K.-F., & Kao, J. C. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2955722 · Cited by 9
Milestones and New Frontiers in Deep Learning
Serpa, Y. R., Pires, L. A., & Rodriguês, M. A. F. (2019) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi-t.2019.00008 · Cited by 11
Image-Based Learning to Measure the Stopped Delay in an Approach of a Signalized Intersection
Shin, J., Roh, S., & Sohn, K. (2019) · IEEE Access · doi:10.1109/access.2019.2955307 · Cited by 10
Bayesian Inferred Self-Attentive Aggregation for Multi-Shot Person Re-Identification
Liu, X., Bi, S., Fang, S., et al. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2957539 · Cited by 9
Representation Learning on Unit Ball with 3D Roto-translational Equivariance
Ramasinghe, S., Khan, S. H., Barnes, N. M., et al. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01278-x · Cited by 9
A multi-agent architecture for mobile sensing systems
Laport, F., Serrano, E., & Bajo, J. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01608-4 · Cited by 9
Peluso, V., Rizzo, R. G., Cipolletta, A., et al. (2019) · International Conference on Social Networks Analysis, Management and Security (SNAMS) · doi:10.1109/snams.2019.8931889 · Cited by 9
S-DenseNet: A DenseNet Compression Model Based on Convolution Grouping Strategy Using Skyline Method
Yu, C., He, X., Ma, H., Qi, X., Lu, J., & Zhao, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2960315 · Cited by 9
Layerwise Noise Maximisation to Train Low-Energy Deep Neural Networks
Henwood, S., Leduc-Primeau, F., & Savaria, Y. (2020) · IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) · doi:10.1109/aicas48895.2020.9073854 · Cited by 12
RRAM-VAC: A Variability-Aware Controller for RRAM-based Memory Architectures
Tuli, S., Rios, M. A., Levisse, A., & David Atienza, E. S. L. (2020) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/asp-dac47756.2020.9045220 · Cited by 9
Conditional Generative Neural Decoding with Structured CNN Feature Prediction
Du, C., Du, C., Huang, L., & He, H. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i03.5647 · Cited by 11
A Robust Deep-Neural-Network-Based Compressed Model for Mobile Device Assisted by Edge Server
Yan, Y., & Pei, Q. (2019) · IEEE Access · doi:10.1109/access.2019.2958406 · Cited by 9
Area Definition and Public Opinion Research of Natural Disaster Based on Micro-blog Data
He, Y., Wen, L., & Zhu, T. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2019.12.030 · Cited by 9
Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients
Ma, C., Wu, B., Fan, Y., Zhang, Y., & Li, Z. (2022) · Machine Intelligence Research · doi:10.1007/s11633-022-1328-1 · Cited by 9
Low-Light Image Enhancement Based On Modified U-Net
Cai, Y., & KinTak, U. (2019) · International Conference on Wavelet Analysis and Pattern Recognition · doi:10.1109/icwapr48189.2019.8946456 · Cited by 12
A PVT-robust Customized 4T Embedded DRAM Cell Array for Accelerating Binary Neural Networks
Shin, H., Sim, J., Lee, D., & Kim, L. (2019) · doi:10.1109/iccad45719.2019.8942072 · Cited by 10
DHA: Supervised Deep Learning to Hash with an Adaptive Loss Function
Xu, J., Guo, C., Liu, Q., Qin, J., Wang, Y., & Liu, L. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00368 · Cited by 10
Wu, B., Feng, S., Sun, G., Xu, L., & Ai, C. (2019) · Journal of Vibroengineering · doi:10.21595/jve.2019.20359 · Cited by 9
Adhikari, A., & Ghimire, S. (2019) · doi:10.1109/aitb48515.2019.8947436 · Cited by 9
An Efficient Algorithm of Facial Expression Recognition by TSG-RNN Network
Huang, K., Li, J., Cheng, S., Yu, J., Tian, W., Zhao, L., Hu, J., & Chang, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-37734-2_14 · Cited by 11
Dizaji, K. G., Gao, H., Yang, Y., Huang, H., & Deng, C. D. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2956523 · Cited by 9
A Multi-Feature Representation of Skeleton Sequences for Human Interaction Recognition
Wang, X., & Deng, H. (2020) · Electronics · doi:10.3390/electronics9010187 · Cited by 9
Design exploration predicts designer creativity: a deep learning approach
Liu, Y., & Liang, C. (2020) · Cognitive Neurodynamics · doi:10.1007/s11571-020-09569-7 · Cited by 11
Conformal prediction based active learning by linear regression optimization
Matiz, S., & Barner, K. E. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.01.018 · Cited by 9
Convolution Neural Network-based Action Recognition for Fall Event Detection
Hassan, M. F. A. (2019) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2019/6881.62019 · Cited by 9
Li, Z., Zhou, A., Wang, M., et al. (2019) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio49542.2019.8961602 · Cited by 9
Deep Learning Methods On Neutron Scattering Data
Song, G., Porcar, L., Boehm, M., et al. (2020) · EPJ Web of Conferences · doi:10.1051/epjconf/202022501004 · Cited by 9
Silva, P. G. S., Lopes, C. A. M., Lima, E. B., Bezerra, B. L. D., & Zanchettin, C. (2019) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2019.00228 · Cited by 7
Intelligent Battlefield Situation Comprehension Method Based On Deep Learning in Wargame
Zhang, J., Xue, Q., Chen, Q., Zhang, Y., Ding, P., & Deng, Q. (2019) · IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT) · doi:10.1109/iccasit48058.2019.8973170 · Cited by 9
Yin, T., Zang, X., Hou, Z., et al. (2020) · Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences · doi:10.24251/hicss.2020.116 · Cited by 9
Zeng, M., Lu, C., Zhang, F., Lu, Z., Wu, F., Li, Y., & Li, M. (2019) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8983279 · Cited by 9
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
