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 69,501–69,600 of 108,435
Zhou, Y., Yang, Y., Wang, D., Zhai, Y., Li, H., & Xu, Y. (2024) · Agronomy · doi:10.3390/agronomy14122869 · Cited by 5
Wu, T., He, R., & Peng, C.-C. (2024) · Remote Sensing · doi:10.3390/rs16234513 · Cited by 4
m3MIMO: An 8×8 mmWave Multi-User MIMO Testbed for Wireless Research
Haque, K. F., Meneghello, F., Rumman, K. M., et al. (2024) · Proceedings of the 28th Annual International Conference on Mobile Computing And Networking · doi:10.1145/3636534.3697321 · Cited by 4
Radiation Pneumonitis Prediction Using Dual-Modal Data Fusion Based on Med3D Transfer Network
Tang, J., Wang, H., Wu, D., Kong, Y., Huang, J., & Han, S. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01339-9 · Cited by 5
A Stability Analysis of Neural Networks and Its Application to Tsunami Early Warning
Rim, D., Suri, S., Hong, S., Lee, K., & LeVeque, R. J. (2024) · Journal of Geophysical Research Machine Learning and Computation · doi:10.1029/2024jh000223 · Cited by 8
Improved U-net network-based segmentation method for fast diffusion measurement
Liu, P., Long, Q., Luo, Y., et al. (2024) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2024.108741 · Cited by 4
Automated Classification of Body MRI Sequences Using Convolutional Neural Networks
Kim, B., Mathai, T. S., Helm, K., et al. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.11.046 · Cited by 4
Bi, Y., Xi, Y., & Jing, C. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.107642 · Cited by 5
InfoPro: Locally Supervised Deep Learning by Maximizing Information Propagation
Wang, Y., Ni, Z., Pu, Y., Zhou, C. L., Ying, J., Song, S., & Huang, G. M. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02296-0 · Cited by 5
Kim, J., Noh, J.-H., Jang, J.-Y., et al. (2024) · Applied Sciences · doi:10.3390/app142411541 · Cited by 4
GCPNet: Gradient-aware channel pruning network with bilateral coupled sampling strategy
Zhang, Z., Cao, C., Zheng, F., Sun, T., & Zhao, L.-J. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126104 · Cited by 4
YOLOv7‐PSAFP: Crop pest and disease detection based on improved YOLOv7
Du, L., Zhu, J., Liu, M., & Wang, L. (2024) · IET Image Processing · doi:10.1049/ipr2.13304 · Cited by 12
Detection and segmentation framework for defect detection on multi‐layer ceramic capacitors
Kim, H., Son, S., & Oh, H. (2024) · ETRI Journal · doi:10.4218/etrij.2024-0066 · Cited by 4
Liu, Y., Chen, M., Zhu, C., Liang, H., & Chen, J. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126143 · Cited by 4
Fault diagnosis method of rolling bearing under variable operating conditions based on MFCCNN
Lei, C., Wan, H., Yu, Y. B., Miao, C., & Feng, R. (2024) · Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · doi:10.1177/09544062241303382 · Cited by 4
Wang, M., Zhou, Y., Yao, S., Wu, J., Zhu, M., Dong, L., & Wang, D. (2024) · Parasites & Vectors · doi:10.1186/s13071-024-06587-w · Cited by 5
Vangety, N., Majee, A., & Roy, S. (2024) · Optical Fiber Technology · doi:10.1016/j.yofte.2024.104109 · Cited by 4
Zhang, Y., Fu, X., & Lin, H. (2024) · Electric Power Systems Research · doi:10.1016/j.epsr.2024.111315 · Cited by 5
Breitmeyer, S. E., Williams, A., Conlon, M. D., Wertz, T. A., Heflin, B. C., Shull, D., & Duris, J. W. (2024) · Toxics · doi:10.3390/toxics12120921 · Cited by 9
Computer Vision Based Waste Classification and Management System
Shahriar, M. S., Shammi, S. J., Ahmed, M., et al. (2024) · doi:10.1109/compas60761.2024.10797178 · Cited by 4
Art Comes From Life: Artistic Image Aesthetics Assessment via Attribute Knowledge Amalgamation
Chen, H., Shao, F., Chai, X., et al. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3520616 · Cited by 4
Advancements in Machine Learning and Deep Learning for Breast Cancer Detection: A Systematic Review
Khan, Z., Botlagunta, M. D., Kumari, G. L. A., Malviya, P., & Botlagunta, M. (2024) · Artificial intelligence · doi:10.5772/intechopen.1008207 · Cited by 4
CNN-Based Nonlinear Response Detection from Time-Sequential Impulse Response of an 18-Story Building
Zhang, A., Wang, X., & Dang, J. (2024) · Journal of Earthquake Engineering · doi:10.1080/13632469.2024.2428763 · Cited by 4
Vision Transformer With Relation Exploration for Pedestrian Attribute Recognition
Tan, H., Tan, Z., Weng, D., Liu, A., Wan, J., Lei, Z., & Li, S. Z. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3521677 · Cited by 6
Symmetric Hallucination With Knowledge Transfer for Few-Shot Learning
Wang, S., Zhang, X., Wang, M., & He, X. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3521802 · Cited by 5
Song, P., Zhao, C., & Huang, B. (2024) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2024.3515174 · Cited by 7
ROS-lite2: Autonomous-driving Software Platform for Clustered Many-core Processor
Tajima, Y., Tsunoda, S., & Azumi, T. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802569 · Cited by 4
Fold bifurcation identification through scientific machine learning
Habib, G., & Horváth, Á. (2024) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2024.134490 · Cited by 5
A multi-agent reinforcement learning based approach for automatic filter pruning
Li, Z., Zuo, X., Song, Y., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-82562-w · Cited by 4
Multi-scale and contrastive learning for pediatric chest radiograph classification tasks
Chen, Q., Lin, Z., Li, X., Zheng, J., Zhang, Y., & Ji, R. (2024) · Displays · doi:10.1016/j.displa.2024.102951 · Cited by 4
Satellite Image-Based Field Compost Detection Using Deep Learning
Jeong, S., Kim, B., Park, S., et al. (2024) · 대한원격탐사학회지 · doi:10.7780/kjrs.2024.40.6.3.3 · Cited by 4
Jiang, Y., Sun, H., Luo, Z., Wang, J., & Xiao, R.-P. (2025) · Oral Surgery Oral Medicine Oral Pathology and Oral Radiology · doi:10.1016/j.oooo.2024.12.020 · Cited by 4
Application of Machine Learning in Cell Detection
Liu, X., Wang, X., & Qian, R. (2025) · Targets · doi:10.3390/targets3010002 · Cited by 4
Underwater Gas Leak Quantification by Convolutional Neural Network Using Images
Caldas, G. L. R., de Souza, M. B., Jr., & Moreira, R. M. (2025) · Processes · doi:10.3390/pr13010118 · Cited by 5
Agnihotri, A., & Kohli, N. (2025) · Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) · doi:10.2174/0123520965334135241115064754 · Cited by 4
Compact planar-waveguide integrated diffractive optical neural network chip
Feng, J., Li, C., Yang, D., Liu, Y., Hu, J., Chen, C., Wang, Y., Lin, J., Wang, L., & Jin, P. (2025) · Advanced Photonics Nexus · doi:10.1117/1.apn.4.1.016010 · Cited by 5
Feasibility study on dose conversion using a deep learning algorithm for retrospective dosimetry
Kim, H., Jeon, B., Kim, M. C., et al. (2025) · Radiation Measurements · doi:10.1016/j.radmeas.2025.107382 · Cited by 4
Waste Management System ECOSORT -AI
Sahana Sharma, M., Patil, A. S., Raksha, H. L., et al. (2025) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem40491 · Cited by 4
Comparative Analysis of Activation Functions in Simple Convolutional Neural Networks
Song, T. (2024) · IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT) · doi:10.1109/iccasit62299.2024.10827900 · Cited by 4
Bivariate DeepKriging for Large-Scale Spatial Interpolation of Wind Fields
Nag, P., Sun, Y., & Reich, B. J. (2025) · Technometrics · doi:10.1080/00401706.2025.2453197 · Cited by 12
Zhao, J., Dong, C., Zhou, Z., et al. (2025) · International Journal of Coal Preparation and Utilization · doi:10.1080/19392699.2025.2453053 · Cited by 4
Cho, J., Ha, J., Song, H., Jang, S., & Jo, K. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110024 · Cited by 4
OmniPlane: A Recolorable Representation for Dynamic Scenes in Omnidirectional Videos
Kou, S., Zhang, F., Nazarenus, J., et al. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3531763 · Cited by 4
Spatio-Temporal Feature Aware Vision Transformers for Real-Time Unmanned Aerial Vehicle Tracking
Zhang, H., Ye, H., Guo, X., Zhang, X., Rong, Y., & Li, S. (2025) · Drones · doi:10.3390/drones9010068 · Cited by 4
Data augmentation for blurred-text image deblurring using non-euclidean extrapolations
Ranjan, A., & Ravinder, M. (2025) · International Journal of Information Technology · doi:10.1007/s41870-024-02334-7 · Cited by 4
Chambi, E. A., Alzamora, D. G., & Angulo, A. (2025) · doi:10.3390/engproc2025083015 · Cited by 4
A Lightweight Detection Model Without Convolutions for Complex Stacked Grasping Tasks
Ren, L., Kang, Y., Jia, H., et al. (2025) · Electronics · doi:10.3390/electronics14030437 · Cited by 4
Li, Y., Yu, T., Luo, J., Li, X., Deng, J., & Yang, Q. (2025) · The Visual Computer · doi:10.1007/s00371-024-03784-7 · Cited by 4
A diffusion-based super resolution model for enhancing sonar images
Bryan, O., Berthomier, T., d'Ales, B., Furfaro, T. C., Haines, T. S. F., Pailhas, Y., & Hunter, A. J. (2025) · The Journal of the Acoustical Society of America · doi:10.1121/10.0034882 · Cited by 6
Modeling of TES Tanks by Means of CFD Simulation Using Neural Networks
Cala, E. F. R., Béjar, R., Mateu, C., Borri, E., Romagnoli, A., & Cabeza, L. F. (2025) · Energies · doi:10.3390/en18030511 · Cited by 5
Wu, X., Xu, J., Li, K., et al. (2025) · Mathematics · doi:10.3390/math13030392 · Cited by 4
Mode-Unified Intent Estimation of a Robotic Prosthesis Using Deep-Learning
Kim, H., Lee, D., Maldonado-Contreras, J. Y., Zhou, S., Herrin, K. R., & Young, A. J. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3535186 · Cited by 4
Source-Free Semantic Regularization Learning for Semi-Supervised Domain Adaptation
Huang, X., Zhu, C., Ren, R., et al. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3535364 · Cited by 4
Seeking the optimal accuracy-rate equilibrium in face recognition
Tian, Y., Ou, F.-Z., Wang, S., et al. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129423 · Cited by 4
Wang, J., Zhang, S., Xu, Y., Zhang, Z., Yang, X., & Cheng, Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126592 · Cited by 6
Robust Distribution-Aware Ensemble Learning for Multi-Sensor Systems
Goodarzi, P., Schauer, J., & Schütze, A. (2025) · Sensors · doi:10.3390/s25030831 · Cited by 8
Capó, M., Vitali, S., Athanasiou, G., Cusimano, N., García, D., Cruickshank, G. S., & Patel, B. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121064 · Cited by 7
Understand and Detect: Multi-step zero-shot detection with image-level specific prompt
Guo, M., Wu, K., Jiang, Z., Wang, H., & Men, A. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113083 · Cited by 5
Toward Federated Customized Neural Architecture Search for Remote Sensing Scene Classification
Li, J., Wang, S., Yang, R., Gong, M., Hu, Z., Zhang, N., Sheng, K., & Zhou, Y. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3537085 · Cited by 5
S3Former: A Deep Learning Approach to High Resolution Solar PV Profiling
Tran, M. Q., Luis, A. D., Liao, H., Huang, Y., McCann, R., Mantooth, H. A., Cothren, J. D., & Le, N. (2025) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2025.3531764 · Cited by 7
SS ViT: Observing pathologies of multi-layer perceptron weights and re-setting vision transformer
Ning, C., & Gan, H. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111422 · Cited by 4
Deep Learning-based Bias Correction Method for Seasonal Prediction of Summer Rainfall in China
Qu, A., Bao, Q., Zhu, Τ., & Zhao-ming, L. U. O. (2024) · Journal of Tropical Meteorology · doi:10.3724/j.1006-8775.2024.040 · Cited by 4
A basic introduction to deep learning
Saha, S., & Ahmad, T. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-326484-9.00010-5 · Cited by 5
Enhancing Symbol Recognition in Library Science via Advanced Technological Solutions
Bernasconi, E., & Ferilli, S. (2025) · Information · doi:10.3390/info16020119 · Cited by 7
Zhao, C. L., Tian, J., Wang, H., Shi, Z., Wang, X., Huang, J., & Tang, L. (2025) · Journal of Nondestructive Evaluation · doi:10.1007/s10921-025-01166-0 · Cited by 4
Wang, J., Gao, G., Tian, C., Zhang, J., Jiao, D., & Liu, Z. (2025) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-025-06105-0 · Cited by 5
Spatial-spectral hyperspectral classification based on learnable 3D group convolution
Li, G., & Ye, M. (2025) · Spectroscopy Letters · doi:10.1080/00387010.2025.2459256 · Cited by 6
Şerbănescu, M.-S., Streba, L., Demetrian, A. D., Gheorghe, A.-G., Mămuleanu, M., Pirici, D. N., & Streba, C. T. (2025) · Cancers · doi:10.3390/cancers17040611 · Cited by 4
Jia, Y., Liu, P., Xiong, T., Han, M., Jia, Q., & Gao, Y. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107696 · Cited by 4
A Review of the Intraoperative Use of Artificial Intelligence in Urologic Surgery
Guduguntla, A., Al-Khanaty, A., Davey, C. E., Patel, O., Ta, A. D., & Ischia, J. J. (2025) · Société Internationale d’Urologie Journal · doi:10.3390/siuj6010005 · Cited by 5
A squeeze‐and‐excitation network for SNR estimation of communication signals
Hu, D., Zhao, Y., Xie, W., et al. (2025) · IET Communications · doi:10.1049/cmu2.70006 · Cited by 4
Detecting Internal Waves From Altimeter Data Using Deep Learning Method
Ma, C., Li, B., Gao, Z., et al. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3541248 · Cited by 4
Şentürk, T., Lati̇foğlu, F., Altıntop, Ç. G., et al. (2025) · Applied Sciences · doi:10.3390/app15041984 · Cited by 4
Omeke, J. E., Misra, S., & Retnanto, A. (2025) · International Petroleum Technology Conference · doi:10.2523/iptc-24670-ms · Cited by 4
Jiang, C., Chen, T., Yang, W. L., et al. (2025) · Applied Physics B · doi:10.1007/s00340-025-08406-1 · Cited by 4
Köhler, M., & Krzyżak, A. (2025) · IEEE Transactions on Information Theory · doi:10.1109/tit.2025.3541181 · Cited by 8
Lin, Z., Zhu, Z., Guo, L., Chen, J., & Wu, J. (2025) · Applied Sciences · doi:10.3390/app15042063 · Cited by 4
Leveraging deep semantic segmentation for assisted weed detection
Garibaldi-Márquez, F., Flores, G., & Valentín-Coronado, L. M. (2025) · Journal of Agricultural Engineering · doi:10.4081/jae.2025.1741 · Cited by 4
Shi, Y., Wang, N., & Ellingwood, B. R. (2025) · Structural Safety · doi:10.1016/j.strusafe.2025.102580 · Cited by 4
Nettop: A light-weight network of orthogonal-plane features for image recognition
Nguyen, T. T., & Nguyễn, T. P. (2025) · Machine Learning · doi:10.1007/s10994-024-06672-2 · Cited by 5
Xiong, S., Liu, S., Zhang, W., et al. (2025) · Diagnostic Pathology · doi:10.1186/s13000-025-01618-1 · Cited by 4
Risk Assessment for UAV Autonomous Landing in Urban Environments Using Semantic Segmentation
Loera-Ponce, J. A., Mercado-Ravell, D., Becerra, I., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-80366-6_17 · Cited by 4
Vision-Based UAV Localization on Various Viewpoints
Ooi, Y.-M., Chang, C.-C., Su, Y., & Chang, C.-M. (2025) · IEEE Access · doi:10.1109/access.2025.3545581 · Cited by 4
Kwok, M. L. J., Kwong, R., Ng, P. M. L., Chan, J. K. Y., & Lau, M. M. (2025) · Online Information Review · doi:10.1108/oir-06-2024-0407 · Cited by 4
A data-physical dual-driven surrogate model for reservoir simulation
Zeng, T., Zeng, N., Deng, C., & Lin, B. (2025) · Physics of Fluids · doi:10.1063/5.0253146 · Cited by 5
Alotaibi, A. S., Contreras, R., Thakker, N., Mahapatro, A., Jala, S. R. A., Mohanty, E., Devulapally, P., Mirchandani, M., Marsool, M. D. M., Jain, S. M., et al. (2025) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000003080 · Cited by 6
AA-mDLAM: An accelerated ADMM-based framework for training deep neural networks
Ebrahimi, Z., Batista, G., & Deghat, M. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129744 · Cited by 9
PALM: Personalized Active Learning for mmWave-Based Activity Recognition
Chiang, H.-C., Wu, Y., Li, G.-H., Shirmohammadi, S., & Hsu, C.-H. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3547487 · Cited by 5
Na, Z., Guo, Z., & Zhu, Y. (2025) · Electronics · doi:10.3390/electronics14051011 · Cited by 4
Salinas, H., Brahm, R., Olmschenk, G., Barry, R., Pichara, K., Silva, S. I., & Araujo, V. (2025) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staf347 · Cited by 13
Zheng, J. L., Li, M., Li, X., Zhang, P., & Wu, Y. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3547822 · Cited by 5
Prediction of surface water pollution using wavelet transform and 1D-CNN
Wang, G., Zhang, H., Gao, M., et al. (2025) · Water Science & Technology · doi:10.2166/wst.2025.032 · Cited by 4
A deep learning framework for solving fractional partial differential equations
Ali, A. H. A., Senu, N., Ahmadian, A., & Wahi, N. (2025) · Physica Scripta · doi:10.1088/1402-4896/adbd8f · Cited by 4
Predicting primary delay of train services using graph-embedding based machine learning
Tang, R., Liu, R., & Lin, Z. (2025) · Journal of Rail Transport Planning & Management · doi:10.1016/j.jrtpm.2025.100518 · Cited by 4
Goto, M., Futamura, Y., Makishima, H., SAITO, T. K., Sakamoto, N., Iijima, T., TAMAKI, Y., Okumura, T., Sakurai, T., & Sakurai, H. (2025) · Journal of Radiation Research · doi:10.1093/jrr/rraf004 · Cited by 5
Daily Wildfire Risk Prediction by Mining Global and local spatio-temporal dependency
Deng, J., Hong, B., Wang, W.-M., & Gu, G. (2025) · Earth Science Informatics · doi:10.1007/s12145-024-01652-5 · Cited by 8
A Spatiotemporal Scheme for Process Control Using Image Analysis of Speckle Patterns
Sabahno, H., & Khodadad, D. (2025) · Quality and Reliability Engineering International · doi:10.1002/qre.3758 · Cited by 4
Hand Pose Detection Using YOLOv8-pose
Kadam, P., Fang, G., Amirabdollahian, F., et al. (2024) · doi:10.1109/icei64305.2024.10912185 · Cited by 4
Kaur, M., Valderrama, C. E., & Liu, Q. (2025) · Machine Learning Science and Technology · doi:10.1088/2632-2153/adc072 · Cited by 4
Creation of Automatic Monitoring Systems Using AutoML Technologies
Sobolevskii, V. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-80710-7_29 · Cited by 4
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
