The DOI matches its Crossref record, with a small difference in title or year; 31 of the 31 references it cites were found in a registry or an index; 2 of the 14 sources its claims cite were checked for support.
ImageNet classification with deep convolutional neural networks
Open accessAbstract
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
42 Own result · 21 Own interpretation · 7 No basis given · 4 No full text of the sources · 3 Sources not found · 2 Supported · 2 Not checked yet · 1 Own method
Every source the paper cites for this claim was checked and supports it.
In the paper
“This technique reduces complex co adaptations of neurons, since a neuron cannot rely on the presence of particular other neurons.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
What the sources say
- Supportedcites [12] 12. Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012). source
The paper states on p. 1 that dropout prevents complex co-adaptations by randomly omitting hidden units, so a hidden unit cannot rely on other hidden units being present. This directly supports the citing sentence. (LLM, conf=0.95)
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“There is, however, a very efficient version of model combination that only costs about a factor of two during training.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
What the sources say
- Supportedcites [12] 12. Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012). source
The paper describes dropout as an efficient way to combine many neural-network models (p. 1). In F.6 Training (p. 17), it reports that ImageNet training took roughly four days with dropout versus two days without it, supporting the approximate factor-of-two training cost. (LLM, conf=0.88)
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This scheme reduces our top-1 and top-5 error rates by 1.7% and 1.2%, respectively, as compared with a net with half as many kernels in each convolutional layer trained on one GPU.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.2. Training on multiple GPUs
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The size of our network made overfitting a significant problem, even with 1.2 million labeled training examples”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“So these data augmentation schemes are, in effect, computationally free.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 18
- Probably not a substantial claim. 26
- The quote only partly states this claim. 2
Create a free account to see these claims.
Create a free account to see these assumptions.
34 more claims
Create a free account to see all 82 claims and to verify the 3 references still open.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In the field of DL, the CNN is the most famous and commonly employed algorithm [30, 71–75].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [30]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Convolutional neural networks (CNNs), built for spatial data, have demonstrated performance comparable to that of specialists in cancer detection and tumor segmentation [15,16,17,18,19,20].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [16]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Deep CNN architectures with 8, even 22 layers [4], [33], can be useful even for CADe problems where the available training datasets are limited.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [4]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Fully convolutional neural networks (fCNNs) have provided state-of-the-art performance for object classification and image segmentation (1).”
From Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning (2018) · cites this paper as [1]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The first wave saw the emergence of deep learning as the dominant paradigm for analyzing high-dimensional biological data.”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [1]
Experimental — accuracy not yet measured
19 more claims about this paper
Create a free account to see every claim other papers make about this one.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
“we found that removing any convolutional layer (each of which contains no more than 1% of the model's parameters) resulted in inferior performance.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.5. Overall architecture
“The third convolutional layer has 384 kernels of size 3 × 3 × 256”
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,884
Self-citationLearning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,501
Self-citation(2010) · International Conference on Machine Learning · Cited by 13,995
Self-citationImproving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
Self-citationFukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759
LabelMe: A Database and Web-Based Tool for Image Annotation
Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0090-8 · Cited by 4,210
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196
Research works printed but not linked: 13
- Berg A., 2010, Large scale visual recognition challenge
- Deng J., 2009, CVPR09
- Krizhevsky A., 2009, Department of Computer Science
- Krizhevsky A., 2010, Convolutional deep belief networks on cifar-10. Unpublished manuscript
- Krizhevsky A., 2011, ESANN
- LeCun Y., 1990, Advances in Neural Information Processing Systems
- LeCun Y., 1985, Une procedure d'apprentissage pour reseau a seuil asymmetrique (a learning scheme for asymmetric threshold networks)
- 10.5555/1896300.1896315 · DOI
- Mensink T., 2012, Italy
- 10.5555/3104322.3104425 · DOI
- Rumelhart D.E., 1985, DTIC Document
- 10.5555/938980.939477 · DOI
- Werbos P., 1974, Beyond regression: New tools for prediction and analysis in the behavioral sciences
Cited by
Items 33,401–33,500 of 108,435
Centeno-Bautista, M. A., Rangel-Rodriguez, A. H., Perez-Sanchez, A. V., et al. (2023) · Applied Sciences · doi:10.3390/app13063569 · Cited by 24
A Data Augmented Method for Plant Disease Leaf Image Recognition based on Enhanced GAN Model Network
Xin, M. Y., Weay, A. L., & Palaniappan, S. (2023) · Journal of Informatics and Web Engineering · doi:10.33093/jiwe.2023.2.1.1 · Cited by 24
An Ensemble DCNNs-Based Regression Model for Automatic Facial Beauty Prediction and Analyzation
Saeed, J. N., Abdulazeez, A. M., & Ibrahim, D. A. (2023) · Traitement du signal · doi:10.18280/ts.400105 · Cited by 24
Asif, M. R., Foged, N., Bording, T. S., Larsen, J. J., & Christiansen, A. V. (2023) · Earth system science data · doi:10.5194/essd-15-1389-2023 · Cited by 25
A Novel Real-Time Autonomous Crack Inspection System Based on Unmanned Aerial Vehicles
Tse, K.-W., Pi, R., Sun, Y., Wen, C., & Feng, Y. (2023) · Sensors · doi:10.3390/s23073418 · Cited by 24
Active learning with deep autoencoders for seismic facies interpretation
Mustafa, A., & AlRegib, G. (2023) · Geophysics · doi:10.1190/geo2022-0353.1 · Cited by 24
An IoMT-Based Melanoma Lesion Segmentation Using Conditional Generative Adversarial Networks
Ali, Z., Naz, S., Zaffar, H., Choi, J., & Kim, Y. (2023) · Sensors · doi:10.3390/s23073548 · Cited by 24
Novel Convolutions for Semantic Segmentation of Remote Sensing Images
Xiao, R., Zhong, C., Zeng, W., Cheng, M., & Wang, C. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3265752 · Cited by 23
Quantitative land price analysis via computer vision from street view images
Zhao, C., Ogawa, Y., Chen, S., Oki, T., & Sekimoto, Y. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106294 · Cited by 26
Detecting Video Inter-Frame Forgeries Based on Convolutional Neural Network Model
Nguyen, X. H., Hu, Y., Amin, M. A., Hayat, K. G., Le, V. T., & Truong, D. (2020) · International Journal of Image Graphics and Signal Processing · doi:10.5815/ijigsp.2020.03.01 · Cited by 25
Metabolic activity organizes olfactory representations
Qian, W. W., Wei, J. N., Sánchez-Lengeling, B., Lee, B. K., Luo, Y., Vlot, M., Dechering, K. J., Peng, J., Gerkin, R. C., & Wiltschko, A. B. (2023) · eLife · doi:10.7554/elife.82502 · Cited by 28
Deep Classification with Linearity-Enhanced Logits to Softmax Function
Shao, H., & Wang, S. (2023) · Entropy · doi:10.3390/e25050727 · Cited by 24
Detection of Anthracnose on Mango Tree Leaf Using Convolutional Neural Network
Yumang, A. N., Samilin, C. J. N., & Sinlao, J. C. P. (2023) · International Conference on Computer and Automation Engineering · doi:10.1109/iccae56788.2023.10111489 · Cited by 24
Li, W., Wang, Z., Mai, R., Ren, P., Zhang, Q., Zhou, Y., Xu, N., Zhuang, J., Xin, B., Gao, L., et al. (2023) · Visual Intelligence · doi:10.1007/s44267-023-00006-x · Cited by 27
Mutual Boost Network for Attributed Graph Clustering
Yan, X., Yu, X., Hu, S., & Ye, Y. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120479 · Cited by 24
Yang, Z., Yan, Z., Diao, W., et al. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3277203 · Cited by 25
Sakeef, N., Scandola, S., Kennedy, C. E., Lummer, C., Chang, J., Uhrig, R. G., & Lin, G. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.05.005 · Cited by 26
Topic sentiment analysis based on deep neural network using document embedding technique
Seilsepour, A., Ravanmehr, R., & Nassiri, R. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05423-9 · Cited by 24
Guo, C., Jiang, L., Yang, F., Yang, Z., & Zhang, X. (2023) · Engineering Structures · doi:10.1016/j.engstruct.2023.116378 · Cited by 24
LKC-Net: large kernel convolution object detection network
Wang, W., Li, S., Shao, J., & Jumahong, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-36724-x · Cited by 27
Youneszade, N., Marjani, M., & Ray, S. K. (2023) · IEEE Access · doi:10.1109/access.2023.3285409 · Cited by 24
Compositional Scene Representation Learning via Reconstruction: A Survey
Yuan, J., Chen, T., Li, B., & Xue, X. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3286184 · Cited by 30
Zhang, Y., Zhong, L., Ding, Y., Yu, H., & Zhai, Z. (2023) · Agriculture · doi:10.3390/agriculture13061264 · Cited by 24
TORR: A Lightweight Blockchain for Decentralized Federated Learning
Ma, X., & Xu, D. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3288078 · Cited by 24
Data-free quantization via mixed-precision compensation without fine-tuning
Chen, J., Bai, S., Huang, T., Wang, M., Tian, G., & Liu, Y. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109780 · Cited by 26
Saini, D. J. B., Sivakami, R., Venkatesh, R., Raghava, C. S., Dwarkanath, P. S., Anwer, T. M. K., Smirani, L. K., Ahammad, S. H., Udayaraju, P., Hossain, M. A., et al. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105180 · Cited by 26
Federated Robustness Propagation: Sharing Adversarial Robustness in Heterogeneous Federated Learning
Hong, J., Wang, H., Wang, Z., & Zhou, J. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i7.25955 · Cited by 28
Future Frame Prediction Network for Human Fall Detection in Surveillance Videos
Li, S., & Song, X. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3276891 · Cited by 25
Street view imagery： Methods and applications based on artificial intelligence
Zhang, F., & Liu, Y. (2021) · National Remote Sensing Bulletin · doi:10.11834/jrs.20219341 · Cited by 27
Guatelli, R., Aubin, V. I., Mora, M., Naranjo-Torres, J., & Mora-Olivari, A. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106700 · Cited by 27
Deep supervision feature refinement attention network for medical image segmentation
Fu, Z., Li, J., Hua, Z., et al. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106666 · Cited by 24
Deep learning for automated fish grading
Jayasundara, J. M. V. D. B., Ramanayake, L., Senarath, N., et al. (2023) · Journal of Agriculture and Food Research · doi:10.1016/j.jafr.2023.100711 · Cited by 24
Detection of Forestry Pests Based on Improved YOLOv5 and Transfer Learning
Liu, D., Lv, F., Guo, J., Zhang, H., & Zhu, L. (2023) · Forests · doi:10.3390/f14071484 · Cited by 24
Sousa, J., Lin, J., Wang, Q., Liu, G., Fan, J.-H., Bai, S.-B., Zhao, H., Pan, H., Wei, W., Rittlinger, V., et al. (2023) · Geo-Spatial Information Science · doi:10.1080/10095020.2023.2234008 · Cited by 24
Ong, P., Yeh, C.-W., Tsai, I., et al. (2023) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2023.123214 · Cited by 24
Wen, C., Lin, X., Ying, Y., Ma, Y., Yu, H. L., Li, X., & Yan, J. (2023) · Waste Management · doi:10.1016/j.wasman.2023.08.004 · Cited by 25
Jiang, Z., Zhang, A., Chen, Z., Ma, C., Yuan, Z., Deng, Y., & Zhang, Y. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02183-4 · Cited by 27
MarginMatch: Improving Semi-Supervised Learning with Pseudo-Margins
Sosea, T., & Caragea, C. (2023) · 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/cvpr52729.2023.01514 · Cited by 26
Soft Augmentation for Image Classification
Liu, Y., Yan, S., Leal-Taixé, L., Hays, J. H., & Ramanan, D. (2023) · 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/cvpr52729.2023.01558 · Cited by 28
MCTNet: A Multi-Scale CNN-Transformer Network for Change Detection in Optical Remote Sensing Images
Li, W., Xue, L., Wang, X., et al. (2023) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion52260.2023.10224182 · Cited by 24
Deep learning methods applied to digital elevation models: state of the art
Ruíz, J. J. R., López, F. J. A., Reinoso-Gordo, J. F., Ureña-Cámara, M. A., & Quesada, F. J. (2023) · Geocarto International · doi:10.1080/10106049.2023.2252389 · Cited by 29
Fishing Vessel Classification in SAR Images Using a Novel Deep Learning Model
Guan, Y., Zhang, X., Chen, S.-W., Liu, G., Jia, Y., Zhang, Y., Gao, G., Zhang, J., Li, Z., & Cao, C. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3312766 · Cited by 30
Taşçı, B., Acharya, M. R., Bayğın, M., Doğan, Ş., Tuncer, T., & Belhouari, S. B. (2023) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103483 · Cited by 26
Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution
Ullah, I., Hasanat, S. M., Aurangzeb, K., Alhussein, M. A., Rizwan, M., & Anwar, M. S. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1487 · Cited by 23
Defect detection for industrial neutron radiographic images based on modified YOLO network
Guo, W. Q., Qiao, S., Zhao, C., et al. (2023) · Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · doi:10.1016/j.nima.2023.168694 · Cited by 24
DeviationGAN: A generative end-to-end approach for the deviation prediction of sheet metal assembly
Liu, Y., Zhao, Y., Lin, Q., Pan, W., Wang, W., & Ge, E. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110822 · Cited by 24
Interpretable neural networks: principles and applications
Liu, Z., & Xu, F. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.974295 · Cited by 28
Excitation creates a distributed pattern of cortical suppression due to varied recurrent input
O’Rawe, J. F., Zhou, Z., Li, A. J., LaFosse, P. K., Goldbach, H. C., & Histed, M. H. (2023) · Neuron · doi:10.1016/j.neuron.2023.09.010 · Cited by 27
Peláez-Rodríguez, C., Pérez‐Aracil, J., Fister, D., Torres-López, R., & Salcedo‐Sanz, S. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122264 · Cited by 27
Nie, Q., Li, C., Yang, J., Yao, Y., Sun, H., Jiang, T., Grzegorzek, M., Chen, A., Chen, H., Hu, W., et al. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107620 · Cited by 24
RetractedMutual-Guided Dynamic Network for Image Fusion
Guan, Y., Xu, R., Yao, M., et al. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612261 · Cited by 25
High Spatial Resolution Implementation Method for OFDR System Based on Convolution Neural Network
Li, S., Xu, Y., Liu, Z., et al. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3332027 · Cited by 24
Öksüz, C., Urhan, O., & Güllü, M. K. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09164-x · Cited by 23
Lei, Q., Zhong, J., Wang, C., & Li, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122552 · Cited by 24
Intelligent Recognition of Voids Behind Tunnel Linings Using Deep Learning and Percussion Sound
Zhang, X., Lin, X., Zhang, W., Feng, Y., Wei, L., Da, Y.-W., & Hu, K. (2023) · Journal of Intelligent Construction · doi:10.26599/jic.2023.9180029 · Cited by 25
Lu, Y., You, K., Zhou, C., et al. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107814 · Cited by 24
Edge-labeling based modified gated graph network for few-shot learning
Zheng, P., Guo, X., Chen, E., Qi, L., & Guan, L. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110264 · Cited by 25
Han, D., & Guo, C. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1328952 · Cited by 25
Enhancing Generalization of Universal Adversarial Perturbation through Gradient Aggregation
Liu, X., Zhong, Y., Zhang, Y., Qin, L., & Deng, W. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00409 · Cited by 35
Towards Effective Instance Discrimination Contrastive Loss for Unsupervised Domain Adaptation
Zhang, Y., Wang, Z., Li, J., Zhuang, J., & Lin, Z. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01046 · Cited by 25
Hasan, M. E., & Wagler, A. (2024) · AI · doi:10.3390/ai5010017 · Cited by 31
Target Recognition for SAR Images Enhanced by Polarimetric Information
Lin, H., Wang, H., Xu, F., et al. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3361931 · Cited by 24
Zhu, Z., Zhao, G., & Zhao, Q. (2024) · Physics of Fluids · doi:10.1063/5.0188550 · Cited by 27
Detection of tuberculosis using customized MobileNet and transfer learning from chest X-ray image
Shome, N., Kashyap, R., & Laskar, R. H. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105063 · Cited by 27
Yamane, T., Chun, P., Dang, J., & Okatani, T. (2024) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2024.2355929 · Cited by 27
Jiang, H., Zhou, W., Lai, M. Y., Wang, S., Liu, X., Li, G., & Cui, J. (2024) · Thin-Walled Structures · doi:10.1016/j.tws.2024.112110 · Cited by 26
ASiT: Local-Global Audio Spectrogram Vision Transformer for Event Classification
Ahmed, S. A. A., Awais, M., Wang, W., Plumbley, M. D., & Kittler, J. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3428908 · Cited by 34
Strategies of Automated Machine Learning for Energy Sustainability in Green Artificial Intelligence
Castellanos–Nieves, D., & García-Forte, L. (2024) · Applied Sciences · doi:10.3390/app14146196 · Cited by 30
Yao, Z., Lin, F., Chai, S., He, W., Dai, L., & Fei, X. (2024) · IEEE International Conference on Sensors, Electronics and Computer Engineering (ICSECE) · doi:10.1109/icsece61636.2024.10729527 · Cited by 33
Skeleton-Free Body Pose Estimation from Depth Images for Movement Analysis
Crabbe, B., Paiement, A., Hannuna, S., et al. (2015) · doi:10.1109/iccvw.2015.49 · Cited by 24
Training Deep Fourier Neural Networks to Fit Time-Series Data
Gashler, M. S., & Ashmore, S. C. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-09330-7_7 · Cited by 27
Seamless Insertion of Pulmonary Nodules in Chest CT Images
Pezeshk, A. X., Sahiner, B., Zeng, R., Wunderlich, A., Chen, W., & Petrick, N. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2445054 · Cited by 25
Leveraging temporal properties of news events for stock market prediction
Yoshihara, A., Seki, K., & Uehara, K. (2015) · Artificial Intelligence Research · doi:10.5430/air.v5n1p103 · Cited by 24
A Deep Feature based Multi-kernel Learning Approach for Video Emotion Recognition
Li, W., Abtahi, F., & Zhu, Z. (2015) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2818346.2830583 · Cited by 23
Learning may need only a few bits of synaptic precision
Baldassi, C., Gerace, F., Lucibello, C., et al. (2016) · Physical review. E · doi:10.1103/physreve.93.052313 · Cited by 24
Beyond Photo-Domain Object Recognition: Benchmarks for the Cross-Depiction Problem
Cai, H., Wu, Q., & Hall, P. M. (2015) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2015.19 · Cited by 23
Scene parsing using inference Embedded Deep Networks
Bu, S., Han, P., Liu, Z., & Han, J. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.01.027 · Cited by 23
Convolutional Neural Networks for Correcting English Article Errors
Sun, C., Jin, X., Lin, L., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-25207-0_9 · Cited by 24
A context-based privacy preserving framework for wearable visual lifeloggers
Zarepour, E., Hosseini, M. K., Kanhere, S. S., et al. (2016) · IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops) · doi:10.1109/percomw.2016.7457057 · Cited by 24
Kampouris, C., Mariolis, I. G., Peleka, G., Skartados, E., Kargakos, A., Triantafyllou, D., & Malassiotis, S. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487307 · Cited by 26
Multimodal Semantic Learning from Child-Directed Input
Lazaridou, A., Chrupała, G., Fernández, R., & Baroni, M. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1043 · Cited by 21
Embedding Sequential Information into Spatiotemporal Features for Action Recognition
Ye, Y., & Tian, Y. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.142 · Cited by 24
Image classification based on convolutional neural networks with cross-level strategy
Liu, Y., Yin, B., Yu, J., & Wang, Z. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3540-x · Cited by 24
Human authentication from ankle motion data using convolutional neural networks
Gadaleta, M., Merelli, L., & Rossi, M. (2016) · IEEE Statistical Signal Processing Workshop (SSP) · doi:10.1109/ssp.2016.7551815 · Cited by 28
Classroom Teaching Assessment Based on Student Emotions
Sahla, K. S., & Thangavel, S. K. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-47952-1_37 · Cited by 24
A visual attention based convolutional neural network for image classification
Chen, Y., Zhao, D., Lv, L., & Li, C. (2016) · World Congress on Intelligent Control and Automation · doi:10.1109/wcica.2016.7578651 · Cited by 23
Does conservation account for splicing patterns?
Wainberg, M., Alipanahi, B., & Frey, B. J. (2016) · BMC Genomics · doi:10.1186/s12864-016-3121-4 · Cited by 22
Harnessing Music-Related Visual Stereotypes for Music Information Retrieval
Schindler, A., & Rauber, A. (2016) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2926719 · Cited by 23
Multi-level Net: A Visual Saliency Prediction Model
Cornia, M., Baraldi, L., Serra, G., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48881-3_21 · Cited by 24
Early detection of combustion instability by neural-symbolic analysis on hi-speed video
Sarkar, S., Lore, K. G., & Sarkar, S. (2015) · neural information processing systems · Cited by 24
Collaborative Filtering With User-Item Co-Autoregressive Models
Du, C., Li, C., Zheng, Y., Zhu, J., & Zhang, B. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11884 · Cited by 34
Learning Deep Convolutional Neural Networks for X-Ray Protein Crystallization Image Analysis
Yann, M. L.-J., & Tang, Y. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10150 · Cited by 24
Deep wavelet network for image classification
Said, S., Jemai, O., Hassairi, S., Ejbali, R., Zaied, M., & Amar, C. B. (2016) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2016.7844359 · Cited by 25
Mu, N., Xu, X., Zhang, X., et al. (2017) · Neural Computing and Applications · doi:10.1007/s00521-017-2870-6 · Cited by 24
RetractedA dyadic multi-resolution deep convolutional neural wavelet network for image classification
Ejbali, R., & Zaied, M. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4523-2 · Cited by 24
Object localization via evaluation multi-task learning
Tian, Y. T., Wang, H., & Wang, X. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.01.098 · Cited by 24
Learning Detection with Diverse Proposals
Azadi, S., Feng, J., & Darrell, T. (2017) · 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.2017.779 · Cited by 20
Spectral Analysis of CNN for Tomato Disease Identification
Fuentes, A., Im, D. H., Yoon, S., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59063-9_4 · Cited by 24
MANHOLE COVER LOCALIZATION IN AERIAL IMAGES WITH A DEEP LEARNING APPROACH
Commandré, B., En-Nejjary, D., Pibre, L., et al. (2017) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-1-w1-333-2017 · Cited by 24
What Can I Do With This Tool? Self-Supervised Learning of Tool Affordances From Their 3-D Geometry
Mar, T., Tikhanoff, V., & Natale, L. (2017) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2017.2717041 · Cited by 23
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
