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 60,601–60,700 of 108,437
Automatic 12-lead ECG Classification Using a Convolutional Network Ensemble
Ribeiro, A. H., Gedon, D., Teixeira, D. M., et al. (2020) · Computing in cardiology · doi:10.22489/cinc.2020.130 · Cited by 7
Defense-friendly Images in Adversarial Attacks: Dataset and Metrics for Perturbation Difficulty
Pestana, C., Liu, W., Glance, D., & Mian, A. S. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00060 · Cited by 8
Recognition of industrial machine parts based on transfer learning with convolutional neural network
Li, Q., & Chen, G. (2021) · PLoS ONE · doi:10.1371/journal.pone.0245735 · Cited by 9
Fast Optimal Transport Artistic Style Transfer
Qiu, T. H., Ni, B., Liu, Z., & Chen, X. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-67832-6_4 · Cited by 7
Improving the Head Pose Variation Problem in Face Recognition for Mobile Robots
Baltanas, S.-F., Ruiz-Sarmiento, J.-R., & González-Jiménez, J. (2021) · Sensors · doi:10.3390/s21020659 · Cited by 7
DCGAN-Based Data Augmentation for Document Classification
Jadli, A., Hain, M., Chergui, A., et al. (2020) · IEEE International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS) · doi:10.1109/icecocs50124.2020.9314379 · Cited by 7
Improved Loss Function for Image Classification
Wen, C., Yang, X., Zhang, K., et al. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/6660961 · Cited by 7
Meng, X., Peng, Y., & Guo, Y. (2021) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2021.01.005 · Cited by 7
Wind Turbine Gearbox Fault Detection based on Dilated Convolutional Neural Networks
He, J., Wu, P., Gao, J., et al. (2020) · International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS) · doi:10.1109/iccss52145.2020.9336913 · Cited by 7
Recognizing COVID-19 positive: through CT images
Wu, X., Wang, Z., & Hu, S. (2020) · Chinese Automation Congress · doi:10.1109/cac51589.2020.9326470 · Cited by 7
ICodeNet - A Hierarchical Neural Network Approach For Source Code Author Identification
Bora, P., Awalgaonkar, T., Palve, H., Joshi, R. B., & Goel, P. (2021) · International Conference on Machine Learning and Computing (ICMLC) · doi:10.1145/3457682.3457709 · Cited by 8
Wan, Y., Zhong, Y., Ma, A., et al. (2020) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss39084.2020.9323429 · Cited by 7
Plant Taxonomy in Hainan Based on Deep Convolutional Neural Network and Transfer Learning
Liu, W., Feng, W., Huang, M., et al. (2020) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom50675.2020.00197 · Cited by 7
Su, L., Yang, X., Cao, B., Wang, Y., Li, X., & Lu, W. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1754/1/012170 · Cited by 7
Helping to detect legal swimming pools with Deep Learning and Data Visualization
Lima, B., Ferreira, L., & Moura, J. M. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.01.301 · Cited by 7
Research on adaptive local feature enhancement in convolutional neural networks
Sun, T., Shao, C., Liao, H., Ding, S., & Xu, X. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2020.0591 · Cited by 7
Resolving Place Recognition Inconsistencies Using Intra-Set Similarities
Neubert, P., Schubert, S., & Protzel, P. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3060729 · Cited by 7
Deep Learning for Medical Materials: Review and Perspective
Qi, Y., & Liu, C. (2021) · ES Materials & Manufacturing · doi:10.30919/esmm5f426 · Cited by 8
Medical Diagnostic by Data Bagging for Various Instances of Neural Network
Khan, Z., Alvi, M. U. T., Tahir, M. A., & Memon, S. M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68793-9_21 · Cited by 7
Z-Net: an Anisotropic 3D DCNN for Medical CT Volume Segmentation
Li, P., Zhou, X.-Y., Wang, Z., et al. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341426 · Cited by 7
Adaptive Precision Training for Resource Constrained Devices
Huang, T., Luo, T., & Zhou, J. T. (2020) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs47774.2020.00185 · Cited by 11
Classification of Martian Terrains via Deep Clustering of Mastcam Images
Parente, M. C., & Panambur, T. (2020) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss39084.2020.9323206 · Cited by 7
Yuan, Z., & Lin, C. (2021) · 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA) · doi:10.1109/icpeca51329.2021.9362662 · Cited by 7
Chen, Y., Wu, D., Duan, L., et al. (2021) · Physics of Fluids · doi:10.1063/5.0036762 · Cited by 7
DeepSegment: Segmentation of motion capture data using deep convolutional neural network
Yasin, H., & Hayat, S. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104147 · Cited by 7
A Deep Learning-Based Approach for Automatic Leather Classification in Industry 4.0
Pazzaglia, G., Martini, M., Rosati, R., Romeo, L., & Frontoni, E. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68799-1_48 · Cited by 8
Captcha Recognition Based on Deep Learning
Zhou, L. J., Wang, J., Lu, W., Yang, F., Zhang, R., & Zhang, L. (2020) · International Conference Big Data Research · doi:10.1145/3445945.3445961 · Cited by 8
COMPUTATION COMPLEXITY OF DEEP RELU NEURAL NETWORKS IN HIGH-DIMENSIONAL APPROXIMATION
Dũng, Ð., Nguyen, V. K., & Thao, M. X. (2021) · Journal of Computer Science and Cybernetics · doi:10.15625/1813-9663/37/3/15902 · Cited by 5
Efficient 3D Neural Networks with Support Vector Machine for Hippocampus Segmentation
Chen, Y., Yang, X., Cheng, K., et al. (2020) · International Conference on Artificial Intelligence and Computer Engineering (ICAICE) · doi:10.1109/icaice51518.2020.00071 · Cited by 7
Gradient‐Sensitive Optimization for Convolutional Neural Networks
Liu, Z., Feng, R., Li, X., Wang, W., & Wu, X. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/6671830 · Cited by 10
Airport network traffic prediction in 5G scenarios: a deep learning approach
Ji, S., Yang, H., Gong, L., Li, Z., Kadoch, M., & Cheriet, M. (2020) · IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) · doi:10.1109/bmsb49480.2020.9379722 · Cited by 8
(2021) · doi:10.1007/978-3-030-90539-2_2 · Cited by 11
Schuir, J., Brinkhege, R., Anton, E., Oesterreich, T. D., Meier, P., & Teuteberg, F. (2021) · Lecture notes in information systems and organisation · doi:10.1007/978-3-030-86797-3_14 · Cited by 9
Balancing Heterogeneous Image Quality for Improved Cross-Spectral Face Recognition
Cao, Z., Cen, X., Zhao, H., et al. (2021) · Sensors · doi:10.3390/s21072322 · Cited by 7
Efficient Attention Mechanism for Dynamic Convolution in Lightweight Neural Network
Ding, E., Cheng, Y., Xiao, C., et al. (2021) · Applied Sciences · doi:10.3390/app11073111 · Cited by 7
Skip-connected network with gram matrix for product image retrieval
Dai, Y., Li, Y., Sun, B., & Liu, L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.067 · Cited by 7
AEVRNet: Adaptive exploration network with variance reduced optimization for visual tracking
Yang, Y., Xing, W., Wang, D., Zhang, S., Yu, Q., & Wang, L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.118 · Cited by 7
Mobile Attendance based on Face Detection and Recognition using OpenVINO
Brown, D. (2021) · doi:10.1109/icais50930.2021.9395836 · Cited by 7
MLANet: Multi-Layer Anchor-free Network for generic lesion detection
Liu, Z., Xie, X., Song, Y., Zhang, Y., Liu, X., Zhang, J., & Sheng, V. S. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104255 · Cited by 7
Towards Automated Keratoconus Screening Approach using Lateral Segment Photographed Images
Zaki, W. M. D. W., Daud, M. M., Saad, A. H., Hussain, A., & Mutalib, H. A. (2021) · IEEE-EMBS Conference on Biomedical Engineering and Sciences · doi:10.1109/iecbes48179.2021.9398781 · Cited by 8
An Invoice Recognition System Using Deep Learning
Shi, S., Cui, C., & Xiao, Y. L. (2020) · International Conference on Intelligent Computing, Automation and Systems (ICICAS) · doi:10.1109/icicas51530.2020.00093 · Cited by 9
Deng, F., Tie, C., Zeng, Y., et al. (2021) · Journal of X-Ray Science and Technology · doi:10.3233/xst-210841 · Cited by 7
ElSayed, Z., Zaghloul, Z. S., & Li, C. (2021) · IEEE SoutheastCon · doi:10.1109/southeastcon45413.2021.9401815 · Cited by 7
Classification method of IPv6 traffic based on convolutional neural network
Ying, P., Shihui, W., & Xing, X. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1883/1/012088 · Cited by 7
Is Gender “In-the-Wild” Inference Really a Solved Problem?
Roxo, T., & Proença, H. P. M. C. (2021) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2021.3100926 · Cited by 7
A Convolutional Encoder Network for Intrusion Detection in Controller Area Networks
Zhang, X., Cui, X., Cheng, K., et al. (2020) · International Conference on Computational Intelligence and Security (CIS) · doi:10.1109/cis52066.2020.00084 · Cited by 7
Multi-Modal Label Dequantized Gaussian Process Latent Variable Model for Ordinal Label Estimation
Matsumoto, M., Maeda, K., Saito, N., et al. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9415090 · Cited by 7
Applied Artificial Intelligence in NDE
Osman, A., Duan, Y.-X., & Kaftandjian, V. (2021) · Springer eBooks · doi:10.1007/978-3-030-48200-8_49-1 · Cited by 11
Real-Time Ship Motion Forecasting Using Deep Learning
Rashid, M. H., Zhang, J., & Zhao, M. (2021) · The 2nd International Conference on Computing and Data Science · doi:10.1145/3448734.3450923 · Cited by 7
Extending Single Beam Lidar To Full Resolution By Fusing with Single Image Depth Estimation
Lu, Y., Wang, Y., Parikh, D., Yuan, X., & Lu, G. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412565 · Cited by 7
Kovalský, K., & Palamas, G. (2021) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-76426-5_13 · Cited by 7
DSNet: Deep Shadow Network for Illumination Estimation
Xiong, Y., Hongrui, C., Wang, J., et al. (2021) · doi:10.1109/vr50410.2021.00039 · Cited by 7
A Driver’s Visual Attention Prediction Using Optical Flow
Kang, B., & Lee, Y. (2021) · Sensors · doi:10.3390/s21113722 · Cited by 7
DLFusion: An Auto-Tuning Compiler for Layer Fusion on Deep Neural Network Accelerator
Liu, Z., Leng, J., Chen, Q., et al. (2020) · IEEE International Conference on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom) · doi:10.1109/ispa-bdcloud-socialcom-sustaincom51426.2020.00041 · Cited by 7
Performance Analysis of Deep Learning Frameworks for COVID 19 Detection
Naviwala, M. H., & Qureshi, R. (2021) · International Conference on Digital Futures and Transformative Technologies (ICoDT2) · doi:10.1109/icodt252288.2021.9441537 · Cited by 7
Oh, S., Bae, C., Cho, J., Lee, S., & Jung, Y. (2021) · Sensors · doi:10.3390/s21113906 · Cited by 7
Application of MobileNet-v1 for Potato Plant Disease Detection Using Transfer Learning
Mishra, S., Singh, A., & Singh, V. P. (2021) · doi:10.1145/3456389.3456403 · Cited by 7
Deep triplet residual quantization
Zhou, C. S., Po, L.-M., Ou, W., et al. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115467 · Cited by 7
Emotion recognition at the edge with AI specific low power architectures
Nardo, E. D., Santopietro, V., & Petrosino, A. (2021) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2021.104299 · Cited by 7
Ursuleanu, T. F., Luca, A. R., Gheorghe, L., Grigorovici, R., Iancu, S., Hlusneac, M., Preda, C., & Grigorovici, A. (2021) · E-Health Telecommunication Systems and Networks · doi:10.4236/etsn.2021.102003 · Cited by 8
Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent
Nadiradze, G., Markov, I., Chatterjee, B., Kungurtsev, V., & Alistarh, D. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i10.17092 · Cited by 9
Spatio-temporal prediction of soil deformation in bucket excavation using machine learning
Saku, Y., Aizawa, M., Ooi, T., & Ishigami, G. (2021) · Advanced Robotics · doi:10.1080/01691864.2021.1943521 · Cited by 6
Characters Recognition based on CNN-RNN architecture and Metaheuristic
Keddous, F. E., Nguyen, H.-N., & Nakib, A. (2021) · IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · doi:10.1109/ipdpsw52791.2021.00082 · Cited by 7
Alexakis, E. B., & Armenakis, C. (2021) · Canadian Journal of Remote Sensing · doi:10.1080/07038992.2021.1922880 · Cited by 7
BraggNet: Complex Photonic Integrated Circuit Reconstruction Using Deep Learning
Cauchon, J., Vallée, J.-M., St-Yves, J., et al. (2021) · IEEE Journal of Selected Topics in Quantum Electronics · doi:10.1109/jstqe.2021.3096421 · Cited by 7
PIMCaffe: Functional Evaluation of a Machine Learning Framework for In-Memory Neural Processing Unit
Jeon, W., Lee, J., Kang, D., Kal, H., & Ro, W. W. (2021) · IEEE Access · doi:10.1109/access.2021.3094043 · Cited by 7
Li, J., Zhang, C., Wang, L., Ding, P., Hu, L., Yan, B., & Tong, L. (2021) · Brain Sciences · doi:10.3390/brainsci11081004 · Cited by 8
Kamiyama, M., Shimizu, K., & AKINIWA, Y. (2021) · Mechatronics · doi:10.1016/j.mechatronics.2021.102606 · Cited by 7
Haque, S., Liu, A. W., Liu, S., & Chan, J. H. (2021) · Advances in Information Technology · doi:10.1145/3468784.3470655 · Cited by 7
A Deep CNN-based Relay Selection in EH Full-Duplex IoT Networks with Short-Packet Communications
Nguyen, T.-V., Huynh‐The, T., & An, B. (2021) · International Conference on Communications · doi:10.1109/icc42927.2021.9500787 · Cited by 7
Deng, Z., Zhu, Q., He, P., Zhang, D., & Luo, Y. (2021) · Security and Communication Networks · doi:10.1155/2021/6854586 · Cited by 7
ProAI: An Efficient Embedded AI Hardware for Automotive Applications – a Benchmark Study
Mantowsky, S., Heuer, F., Bukhari, S. S., Keckeisen, M., & Schneider, G. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00113 · Cited by 7
gaCNN: Composing CNNs and GAs to Build an Optimized Hybrid Classification Architecture
Mendes, R. D. L., Alves, A. H. D. S., Gomes, M. D. S., Bertarini, P. L. L., & do Amaral, L. R. (2021) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec45853.2021.9504850 · Cited by 7
CJAM: Convolutional Neural Network Joint Attention Mechanism in Gait Recognition
Jia, P., Zhao, Q., Boze, L. I., & Zhang, J. (2021) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2020bdp0010 · Cited by 7
A three-level hierachical framework for additive manufacturing
Ren, Y. M., Ding, Y., Zhang, Y., & Christofides, P. D. (2021) · Digital Chemical Engineering · doi:10.1016/j.dche.2021.100001 · Cited by 7
Zhang, Y., Chen, H., Yang, K., et al. (2021) · IEEE International Conference on Real-time Computing and Robotics (RCAR) · doi:10.1109/rcar52367.2021.9517086 · Cited by 7
Enhanced AlexNet with Super-Resolution for Low-Resolution Face Recognition
Tan, J. C., Lim, K. M., & Lee, C. P. (2021) · doi:10.1109/icoict52021.2021.9527433 · Cited by 7
Urban management image classification approach based on deep learning
Kang, Q., & Xiong, D. (2021) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-210609 · Cited by 7
Artificial Intelligence and Deep Learning, Important Tools in Assisting Gastroenterologists
Luca, M., Ciobanu, A., Barbu, T., & Drug, V. L. (2021) · Intelligent systems reference library · doi:10.1007/978-3-030-79161-2_8 · Cited by 7
Efficient Shot Detector: Lightweight Network Based on Deep Learning Using Feature Pyramid
Park, C., Lee, S.-H., & Han, H. (2021) · Applied Sciences · doi:10.3390/app11188692 · Cited by 7
Multi-Class Strategies for Joint Building Footprint and Road Detection in Remote Sensing
Ayala, C., Aranda, C., & Galar, M. (2021) · Applied Sciences · doi:10.3390/app11188340 · Cited by 5
Ensemble-LungMaskNet: Automated Lung Segmentation using Ensembled Deep Encoders
Öksüz, C., Urhan, O., & Güllü, M. K. (2021) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista52262.2021.9548367 · Cited by 7
Semantically-Aware Strategies for Stereo-Visual Robotic Obstacle Avoidance
Hong, J., de Langis, K., Wyethv, C., Walaszek, C., & Sattar, J. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561863 · Cited by 10
Auto-learning Convolution-Based Graph Convolutional Network for Medical Relation Extraction
Qian, M., Wang, J., Lin, H., Zhao, D., Zhang, Y., Tang, W., & Yang, Z. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88189-4_15 · Cited by 8
Yuan, F., Neubert, P., Schubert, S., et al. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9562087 · Cited by 7
Pan, Y., He, F., Yan, X., & Li, H. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104494 · Cited by 7
The Case for Storage Optimization Decoupling in Deep Learning Frameworks
Macedo, R., Correia, C., Dantas, M., Brito, C. V., Xu, W., Tanimura, Y., Haga, J., & Paulo, J. (2021) · IEEE International Conference on Cluster Computing (CLUSTER) · doi:10.1109/cluster48925.2021.00096 · Cited by 8
Sensing Anomalies as Potential Hazards: Datasets and Benchmarks
Mantegazza, D., Redondo, C., Espada, F., Gambardella, L. M., Giusti, A., & Guzzi, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15908-4_17 · Cited by 8
Bravo-Rocca, G., Liu, P., Guitart, J., Dholakia, A., Ellison, D. D., Falkanger, J., & Hodak, M. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117232 · Cited by 9
Alsaedi, M. A., Mohialdeen, A. S., & Albaker, B. M. (2021) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v10i6.2802 · Cited by 7
Coupling Matrix Extraction of Microwave Filters by Using One-Dimensional Convolutional Autoencoders
Zhang, Y., Wang, Y., Yi, Y., Wang, J., Liu, J., & Chen, Z. (2021) · Frontiers in Physics · doi:10.3389/fphy.2021.716881 · Cited by 8
Multi-Class Parrot Image Classification Including Subspecies with Similar Appearance
Jang, W., & Lee, E. C. (2021) · Biology · doi:10.3390/biology10111140 · Cited by 9
Chunjiao, X. I. A. O., Yu, L., ZHANG, H., et al. (2020) · National Remote Sensing Bulletin · doi:10.11834/jrs.20208298 · Cited by 7
Sheu, M., Morsalin, S. M. S., Zheng, J., et al. (2021) · Sensors · doi:10.3390/s21217399 · Cited by 7
Robust Ensembling Network for Unsupervised Domain Adaptation
Sun, H., Lin, L., Liu, N., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-89363-7_40 · Cited by 7
Electrocardiogram Classification Based on Deep Convolutional Neural Networks: A Review
admin, A., & Abdulazeez, A. M. (2021) · Fusion Practice and Applications · doi:10.54216/fpa.030103 · Cited by 7
Yin, Y., Tran, A. T. T., Zhang, Y., et al. (2021) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/3474717.3483917 · Cited by 7
Korichi, M., Samai, D., Meraoumia, A., et al. (2021) · International Conference on Recent Advances in Mathematics and Informatics (ICRAMI) · doi:10.1109/icrami52622.2021.9585911 · Cited by 7
Drone Model Identification by Convolutional Neural Network from Video Stream
Wisniewski, M., Rana, Z. A., & Petrunin, I. V. (2021) · IEEE/AIAA Digital Avionics Systems Conference (DASC) · doi:10.1109/dasc52595.2021.9594392 · Cited by 7
Fine-Grained Vehicle Classification in Urban Traffic Scenes Using Deep Learning
Najeeb, S. A., Raza, R. H., Yusuf, A., et al. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-8129-5_138 · Cited by 7
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
