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 66,001–66,100 of 108,435
Quantitative Stock Selection Model Using Graph Learning and a Spatial–Temporal Encoder
Cao, T., Wan, X., Wang, H., Yu, X. W., & Xu, L. (2024) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer19030086 · Cited by 8
Robust Detection of Cracked Eggs Using a Multi-Domain Training Method for Practical Egg Production
Fan, W., Huang, Y., Zhang, J., et al. (2024) · Foods · doi:10.3390/foods13152313 · Cited by 5
Deep Learning for High-Speed Lightning Footage—A Semantic Segmentation Network Comparison
Cross, T., Smit, J. R., Schumann, C., et al. (2024) · Atmosphere · doi:10.3390/atmos15080873 · Cited by 5
Optimizing Data Flow in Binary Neural Networks
Vorabbi, L., Maltoni, D., & Santi, S. (2024) · Sensors · doi:10.3390/s24154780 · Cited by 5
Interpretable Deep Learning Model for Tuberculosis Detection Using X-Ray Images
Ahamed, M. F., Nahiduzzaman, M., Islam, M. R., Rahman, T., Islam, K. R., Altyeb, A. B. A., & Chowdhury, M. E. H. (2024) · Springer eBooks · doi:10.1007/978-3-031-59967-5_8 · Cited by 5
Nieberl, M., Zeiser, A., & Timinger, H. (2024) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai59869.2024.00018 · Cited by 5
Pandey, S., & Panda, P. R. (2024) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3677178 · Cited by 7
Shang, T. M., Yang, J., Ge, J., et al. (2024) · Polymer Composites · doi:10.1002/pc.28798 · Cited by 5
Miao, J., Chang, Y., Chen, C., et al. (2024) · Journal of Shanghai Jiaotong University (Science) · doi:10.1007/s12204-024-2761-9 · Cited by 5
Zeng, Z., Yang, Y., Yuan, J., et al. (2024) · Welding in the World · doi:10.1007/s40194-024-01818-1 · Cited by 5
Transformer-based end-to-end attack on text CAPTCHAs with triplet deep attention
Zhang, B., Xiong, Y.-J., Xia, C., & Gao, Y. (2024) · Computers & Security · doi:10.1016/j.cose.2024.104058 · Cited by 5
Exploring artificial intelligence role in improving service building engagement in sorting
Bouabdallaoui, Y., Ducoulombier, L., Lafhaj, Z., & Yim, P. (2023) · Waste Management · doi:10.1016/j.wasman.2024.07.031 · Cited by 6
Artificial intelligence-empowered assessment of bile duct stone removal challenges
Wang, Z., Yuan, H., Lin, K., Zhang, Y., Xue, Y., Liu, P., Chen, Z., & Wu, M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125146 · Cited by 5
Zhang, W., Li, R., Quan, P., Chang, J., Bai, Y., & Su, B. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3445965 · Cited by 6
Compound facial expressions recognition approach using DCGAN and CNN
Ullah, S., Ou, J., Xie, Y., & Tian, W. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20138-6 · Cited by 8
Ail, B. E., Ramele, R., Gambini, J., & Santos, J. (2024) · Brain Science · doi:10.3390/brainsci14080836 · Cited by 5
Deep estimation of the intensity and timing of natural selection from ancient genomes
Laval, G., Patin, É., Quintana‐Murci, L., & Kerner, G. (2024) · Molecular Ecology Resources · doi:10.1111/1755-0998.14015 · Cited by 8
Kang, K.-S., Seo, J., & Ryu, H.-G. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.110650 · Cited by 5
A Machine Walks into an Exhibit: A Technical Analysis of Art Curation
von Davier, T. Ş., Herman, L. M., & Moruzzi, C. (2024) · Arts · doi:10.3390/arts13050138 · Cited by 7
Vis-YOLO: a lightweight and efficient image detector for unmanned aerial vehicle small objects
Deng, X., & Du, J. (2024) · Journal of Electronic Imaging · doi:10.1117/1.jei.33.5.053003 · Cited by 6
AutoRIC: Automated Neural Network Repairing Based on Constrained Optimization
Sun, X., Liu, W., Wang, S., Chen, T., Tao, Y., & Mao, X. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3690634 · Cited by 7
Gao, F., Lü, W., Gan, L., & Xu, C. (2024) · International Journal of Digital Earth · doi:10.1080/17538947.2024.2398066 · Cited by 5
Advancing Ear Biometrics: Enhancing Accuracy and Robustness Through Deep Learning
Mohamed, Y. H., Youssef, Z., Heakl, A., et al. (2024) · Intelligent Methods, Systems, and Applications (IMSA) · doi:10.1109/imsa61967.2024.10652851 · Cited by 5
A novel Complementary Dual-aware Network for point cloud classification
Hu, R., Cao, F., & Wang, W. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109224 · Cited by 6
MUSTER: A Multi-Scale Transformer-Based Decoder for Semantic Segmentation
Xu, J., Shi, W., Gao, P., Li, Q., & Wang, Z. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3449911 · Cited by 9
Qian, J., Ge, H., Lu, Y., & Shan, W. (2024) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2024.3451332 · Cited by 8
Lu, Y., Lin, L., Dong, W., et al. (2024) · Agriculture · doi:10.3390/agriculture14091553 · Cited by 5
Improved deep neural network (EnhanceNet) for real-time detection of some publicly prohibited items
Ejiyi, C. J., Qin, Z., Ukwuoma, C. C., et al. (2024) · Network Computation in Neural Systems · doi:10.1080/0954898x.2024.2398531 · Cited by 5
A deep learning approach to prediction of blood group antigens from genomic data
Moslemi, C., Sækmose, S. G., Larsen, R., Brodersen, T., Bay, J. T., Didriksen, M., Nielsen, K. R., Bruun, M. T., Dowsett, J., Dinh, K. M., et al. (2024) · Transfusion · doi:10.1111/trf.18013 · Cited by 10
FM‐YOLOv8:Lightweight gesture recognition algorithm
Li, F., Na, X., Shi, J., et al. (2024) · IET Image Processing · doi:10.1049/ipr2.13229 · Cited by 5
A fully locally selective large kernel network for traffic video detection
Hou, Y., Zhang, Z., Du, L., & Yin, J. (2024) · Measurement · doi:10.1016/j.measurement.2024.115779 · Cited by 5
Alhajlah, M. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01425-y · Cited by 5
AI4CRC: A Deep Learning Approach Towards Preventing Colorectal Cancer
Fanijo, S. O. (2024) · Journal of Future Artificial Intelligence and Technologies · doi:10.62411/faith.2024-28 · Cited by 8
Sobolev Neural Network With Residual Weighting as a Surrogate in Linear and Non-Linear Mechanics
Kilicsoy, A. O. M., Liedmann, J., Valdebenito, M. A., Barthold, F., & Faes, M. G. R. (2024) · IEEE Access · doi:10.1109/access.2024.3465572 · Cited by 5
Degrees of Freedom Matter: Inferring Dynamics from Point Trajectories
Zhang, Y., Prokudin, S., Mihajlović, M., et al. (2024) · 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/cvpr52733.2024.00197 · Cited by 5
Video Recognition in Portrait Mode
Han, M., Yang, L., Jin, X., Feng, J., Chang, X., & Wang, H. (2024) · 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/cvpr52733.2024.02062 · Cited by 5
SODA: A Dataset for Small Object Detection in UAV Captured Imagery
Pisani, D., Seychell, D., Debono, C. J., & Schembri, M. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10647335 · Cited by 5
Koopcon: A new approach towards smarter and less complex learning
Jebraeeli, V., Jiang, B., Cansever, D. H., et al. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10647948 · Cited by 5
Automated Mineral Identification of Pozzolanic Materials Using XRD Patterns
Kim, H., & Yoon, J. (2024) · Journal of Materials in Civil Engineering · doi:10.1061/jmcee7.mteng-17973 · Cited by 6
Guo, J., He, Q., She, C., Liu, H., & Li, Y. (2024) · Digital Health · doi:10.1177/20552076241283247 · Cited by 5
Spatiotemporal Staggered Projection Imaging for Video-SAR Under the Resource-Constrained Platform
Yang, X., Shi, J., Zhang, B., et al. (2024) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2024.3471731 · Cited by 5
An Exploration of Machine Learning in Art and Design
Dursun, R. Z. A., & Bakan, U. (2024) · Advances in human and social aspects of technology book series · doi:10.4018/979-8-3693-4318-0.ch010 · Cited by 5
Integrating model-driven and data-driven methods for under-frequency load shedding control
Li, B., Wang, H., Li, J., et al. (2024) · Electric Power Systems Research · doi:10.1016/j.epsr.2024.111103 · Cited by 5
Deep-Learning-Based Amplitude Variation with Angle Inversion with Multi-Input Neural Networks
Tao, S., Guo, Y., Huang, H., Li, J., Chen, L., Gui, J., & Zhao, G. (2024) · Processes · doi:10.3390/pr12102259 · Cited by 5
Reverse Stable Diffusion: What prompt was used to generate this image?
Croitoru, F.-A., Hondru, V., Ionescu, R. T., & Shah, M. A. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104210 · Cited by 9
Model and system robustness in distributed CNN inference at the edge
Guo, X., Jiang, Q., Pimentel, A. D., & Stefanov, T. P. (2024) · Integration · doi:10.1016/j.vlsi.2024.102299 · Cited by 7
Person Re-Identification Based on Hybrid Dual-Branch ResNet-Transformer Network
Duan, W., & Wang, Z. (2024) · IEEE International Conference on Computational Intelligence and Applications (ICCIA) · doi:10.1109/iccia62557.2024.10719321 · Cited by 5
Han, J.-Y., Liu, J., & Xu, J. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112400 · Cited by 5
FedCAFE: Federated Cross-Modal Hashing with Adaptive Feature Enhancement
Fu, T., Zhan, Y.-W., Zhang, C.-Y., et al. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681319 · Cited by 5
MetaRepair: Learning to Repair Deep Neural Networks from Repairing Experiences
Xing, Y., Guo, Q., Cao, X., Tsang, I. W.-H., & Ma, L. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680638 · Cited by 5
Morga-Bonilla, S. I., Rivas-Cambero, I., Jimenez, J. T., Téllez-Cuevas, P., Núñez-Cruz, R. S., & Pérez-Arista, O. V. (2024) · World Electric Vehicle Journal · doi:10.3390/wevj15110486 · Cited by 8
Golosov, N., & Cervone, G. (2024) · Remote Sensing · doi:10.3390/rs16213981 · Cited by 8
Deep Learning-Based Road Sign Detection: The Ultimate Road Technology
Singh, A. P., Rahi, P., Sahu, B., et al. (2024) · doi:10.1109/acet61898.2024.10730463 · Cited by 5
Erdoğan, M., & Doğan, M. (2024) · Algorithms · doi:10.3390/a17110506 · Cited by 5
Few-shot learning for inference in medical imaging with subspace feature representations
Liu, J., Fan, K., Cai, X., & Niranjan, M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0309368 · Cited by 9
Zhu, J., Reganti, A., Huang, E. W., et al. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3701563 · Cited by 5
Tang, C., Zhi, X., Sun, J., Wang, S., & Zhang, Y. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112708 · Cited by 5
Automatic Classification of Focal Liver Lesions Based on Multi-Sequence MRI
Hu, M., Wang, S., Wu, M., et al. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01326-0 · Cited by 5
An Overview of Deep Learning for Resource Management in mmWave-NOMA
Ramli, R., & Lee, B. M. (2024) · IEEE Access · doi:10.1109/access.2024.3497137 · Cited by 5
Analysis of the impact of deep learning know-how and data in modelling neonatal EEG
Daly, A., Lightbody, G., & Temko, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-78979-y · Cited by 7
Hessian-based mixed-precision quantization with transition aware training for neural networks
Huang, Z., Han, X., Yu, Z. W., Zhao, Y., Hou, M., & Hu, S. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106910 · Cited by 9
Liang, J., Zhang, Q., & Gu, X. (2024) · Sustainability · doi:10.3390/su162210145 · Cited by 7
Machine learning identification of fractional-order vortex beam diffraction process
Yan, G. U. O., Heng, L. Y. U., Ding, C., et al. (2024) · Acta Physica Sinica · doi:10.7498/aps.74.20241458 · Cited by 5
MRI classification and discrimination of spinal schwannoma and meningioma based on deep learning
Liu, Y., Zhou, Z., & Wang, Y. (2024) · Journal of X-Ray Science and Technology · doi:10.1177/08953996241289745 · Cited by 5
An Unbiased Feature Estimation Network for Few-Shot Fine-Grained Image Classification
Wang, J., Lu, J. G., Yang, J., et al. (2024) · Sensors · doi:10.3390/s24237737 · Cited by 5
Impact of Image Augmentation on Deep Learning-Based Classification of Granite Tiles
Bartos, G. E., Ünaldı, S., & Yalçın, N. (2024) · International Conference on Computer Science and Engineering (UBMK) · doi:10.1109/ubmk63289.2024.10773433 · Cited by 7
Alizadeh, N., Mahdianpari, M., Hemmati, E., & Marjani, M. (2024) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2024.101436 · Cited by 8
Fast Semi-Supervised Learning on Large Graphs: An Improved Green-Function Method
Nie, F., Song, Y., Chang, W., Wang, R., & Li, X. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3518595 · Cited by 5
A Review of CNN and Transformer Applications in Image Processing
Cheng, Z., & Xiaoqin, N. (2024) · International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) · doi:10.1109/iciibms62405.2024.10792839 · Cited by 6
CRE-YOLO: Efficient Jaboticaba Tree Detection on UAV Platforms
Huang, J., Luo, R., Tan, Y., & Wu, Z. (2024) · IEEE Access · doi:10.1109/access.2024.3520115 · Cited by 7
Brainformer: Mimic human visual brain functions to machine vision models via fMRI
Nguyen, X.-B., Li, X., Sinha, P., Khan, S. U., & Luu, K. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129213 · Cited by 14
Fault diagnosis of gear based on URP-CVAE-MGAN under imbalanced and small sample conditions
Yang, N., Liu, J., Ma, H., Zhao, W., & Gao, Y. (2024) · Eksploatacja i Niezawodnosc - Maintenance and Reliability · doi:10.17531/ein/199417 · Cited by 5
aMacP: An adaptive optimization algorithm for Deep Neural Network
Bhakta, S., Nandi, U., Changdar, C., Paul, B., Si, T., & Pal, R. K. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129242 · Cited by 7
Rethinking Masked Representation Learning for 3D Point Cloud Understanding
Wang, C., Zha, Y., He, J., Yang, W., & Zhang, T. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3520008 · Cited by 8
KE-RSIC: Remote Sensing Image Captioning Based on Knowledge Embedding
Cheng, K., Cambria, E., Liu, J., Chen, Y., & Wu, Z. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3523944 · Cited by 11
Qawasmeh, B., Oh, J.-S., & Kwigizile, V. (2024) · Safety · doi:10.3390/safety11010001 · Cited by 5
Xie, J. C., Cui, Y.-A., Guo, R., Chen, H., Guo, Y., & Liu, J. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3526947 · Cited by 6
Zhou, S., Yang, C., Yi, C. X., & Jiao, J. (2025) · Sensors · doi:10.3390/s25020421 · Cited by 5
Deep convolutional neural networks meet variational shape compactness priors for image segmentation
Zhang, K., Li, L., Liu, H., et al. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129395 · Cited by 5
SViG: A Similarity-Thresholded Approach for Vision Graph Neural Networks
Elsharkawi, I., Sharara, H., & Rafea, A. (2025) · IEEE Access · doi:10.1109/access.2025.3531691 · Cited by 7
Aggregate global features into separable hierarchical lane detection transformer
Li, M., Chen, Q., Ge, Z., Tao, F., & Wang, Z. (2025) · Scientific Reports · doi:10.1038/s41598-025-86894-z · Cited by 5
Inception-like Large Kernel network for lightweight image super-resolution
Bai, H. (2025) · Multimedia Systems · doi:10.1007/s00530-024-01652-x · Cited by 5
Han, Z., Zhang, L., Meng, Q., et al. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3532783 · Cited by 5
Local Uncertainty Energy Transfer for Active Domain Adaptation
Sun, Y., Shi, G., Dong, W., Li, X., Dong, L., & Xie, X. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3530788 · Cited by 5
Zhang, L., Jie, Z., Li, Z., Lu, W., Zheng, H., & Wang, W. (2025) · Measurement · doi:10.1016/j.measurement.2025.116867 · Cited by 6
Real‐world UAV recognition based on radio frequency fingerprinting with transformer
Jia, H., Yu, Z., & Yang, J. (2025) · IET Communications · doi:10.1049/cmu2.70004 · Cited by 5
Yan, T., Jin, Y., Liu, S., et al. (2025) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2025.104300 · Cited by 5
Huang, L., & Yang, W. (2025) · Neural Computing and Applications · doi:10.1007/s00521-024-10688-z · Cited by 6
Unified neural network model for predicting optical responses in gold nanostructures
Wang, X., Cao, Y. K., Feng, F., Wang, Z., & Cao, Y. (2025) · Journal of Applied Physics · doi:10.1063/5.0249206 · Cited by 6
The DeepLabV3 + Algorithm Combined With the ResNeXt Network for Medical Image Segmentation
Wu, Y., Xie, Y., & Hong, Y. (2025) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.8386 · Cited by 6
SB‐YOLO‐V8 : A Multilayered Deep Learning Approach for Real‐Time Human Detection
Ansah, K., Appati, J. K., Owusu, E., et al. (2025) · Engineering Reports · doi:10.1002/eng2.70033 · Cited by 5
Gao, G., Lv, Z., Zhang, Y., & Qin, A. K. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107271 · Cited by 6
ML-based semantic segmentation for quantitative spray atomization description
Jose, B., Lammel, O., & Hampp, F. (2025) · International Journal of Multiphase Flow · doi:10.1016/j.ijmultiphaseflow.2025.105179 · Cited by 5
Lightweight FPGA acceleration framework for structurally tailored multi-version MobileNetV1
Lu, X., Zhang, J., Zhu, L., & Tan, X. (2025) · Integration · doi:10.1016/j.vlsi.2025.102383 · Cited by 6
Joint modeling histology and molecular markers for cancer classification
Wang, X., Liu, H., Zhang, Y., Zhao, B., Duan, H., Hu, W., Mou, Y., Price, S., & Li, C. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103505 · Cited by 6
Zhou, H., Cai, D., Lin, L., Huang, D., & Wu, B.-M. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.100867 · Cited by 8
MEAI-Net: Multiview embedding and attention interaction for multivariate time series forecasting
Dong, C.-R., Xu, W., Zhang, F., Hua, Q., & Zhang, Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129769 · Cited by 5
UMIMO: Universal Unsupervised Learning for Mmwave Radar Sensing With MIMO Array Synthesis
Zhang, H., Zhang, D., Song, R., Wu, Z., Chen, J., Fang, L., Lu, Z. P., Hu, Y., Lin, H., & Chen, Y. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3546757 · Cited by 5
Real-Time image enhancement following road scenario classification using deep learning
Anoop, P. P., & Deivanathan, R. (2025) · Measurement · doi:10.1016/j.measurement.2025.117192 · Cited by 5
Deep learning for ultrafast X-ray scattering and imaging with intense X-ray FEL pulses
Hu, M., Fan, J., Tong, Y., Sun, Z., & Jiang, H. (2025) · Advanced Optical Technologies · doi:10.3389/aot.2025.1546386 · Cited by 5
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
