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 36,001–36,100 of 108,435
Tazarv, M., Won, K., Jang, Y., et al. (2022) · Engineering Structures · doi:10.1016/j.engstruct.2022.115002 · Cited by 21
Mahmoud, S., Billing, E., Svensson, H., & Thill, S. (2022) · Cognitive Systems Research · doi:10.1016/j.cogsys.2022.09.005 · Cited by 22
Zheng, Q., Xu, Y., Zhang, P., et al. (2022) · Flow Measurement and Instrumentation · doi:10.1016/j.flowmeasinst.2022.102249 · Cited by 21
Feature Selection Techniques for Big Data Analytics
Albattah, W., Khan, R. U., Alsharekh, M. F., & Khasawneh, S. F. (2022) · Electronics · doi:10.3390/electronics11193177 · Cited by 23
Few-Shot Fine-Grained Image Classification via GNN
Zhou, X., Zhang, Y., & Wei, Q. (2022) · Sensors · doi:10.3390/s22197640 · Cited by 21
A Three-Dimensional Geological Structure Modeling Framework and Its Application in Machine Learning
Wang, S., Cai, Z., Si, X., & Cui, Y. (2022) · Mathematical Geosciences · doi:10.1007/s11004-022-10027-9 · Cited by 24
Yazdi, H., Berenji, S. S., Ludwig, F., et al. (2022) · Heritage · doi:10.3390/heritage5040159 · Cited by 21
Ling, Y., Ying, S., Xu, L., Peng, Z., Mao, X., Chen, Z., Ni, J., Liu, Q., Gong, S., & Kong, D. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.960178 · Cited by 21
The Influence of Image Degradation on Hyperspectral Image Classification
Li, C., Li, Z., Liu, X., & Shutao Li, A. (2022) · Remote Sensing · doi:10.3390/rs14205199 · Cited by 22
Auto-tuning Fixed-point Precision with TVM on RISC-V Packed SIMD Extension
Yang, C.-C., Chen, Y.-R., Liao, H.-H., Chang, Y., & Lee, J. (2022) · ACM Transactions on Design Automation of Electronic Systems · doi:10.1145/3569939 · Cited by 24
A Soft Label Deep Learning to Assist Breast Cancer Target Therapy and Thyroid Cancer Diagnosis
Wang, C., Lin, K., Lin, Y., Khalil, M.-A., Chu, K.-L., & Chao, T. (2022) · Cancers · doi:10.3390/cancers14215312 · Cited by 23
WMNN: Wearables-Based Multi-Column Neural Network for Human Activity Recognition
Tang, C., Chen, X., Gong, J., Occhipinti, L. G., & Gao, S. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3219364 · Cited by 23
Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning
Lee, J., Kim, J. N., Gomez-Perez, L., et al. (2022) · Bioengineering · doi:10.3390/bioengineering9110648 · Cited by 21
Liu, Y., Lian, L., Zhang, E., Xu, L., Xiao, C., Zhong, X., Li, F., Jiang, B., Dong, Y., Ma, L., et al. (2022) · Frontiers in Computer Science · doi:10.3389/fcomp.2022.1036934 · Cited by 24
Marasco, E., & Vurity, A. (2022) · Applied Sciences · doi:10.3390/app122211409 · Cited by 21
Classification of Standard FASHION MNIST Dataset Using Deep Learning Based CNN Algorithms
Xhaferra, E., Cina, E., & Toti, L. (2022) · International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) · doi:10.1109/ismsit56059.2022.9932737 · Cited by 24
Hierarchical organization of objects in scenes is reflected in mental representations of objects
Turini, J., & Võ, M. L. ‐. (2022) · Scientific Reports · doi:10.1038/s41598-022-24505-x · Cited by 25
Maqsood, M., Yasmin, S., Gillani, S. A., Aadil, F., Mehmood, I., Rho, S., & Yeo, S.-S. (2022) · ISA Transactions · doi:10.1016/j.isatra.2022.11.016 · Cited by 22
Han, Q., Hou, M., Wang, H., et al. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106353 · Cited by 21
Zhang, Z., Zhu, X., Zhao, D., Arun, P. V., Zhou, H., Qian, K., & Hu, J. (2022) · Remote Sensing · doi:10.3390/rs14235958 · Cited by 21
Analysis of automatic image classification methods for Urticaceae pollen classification
Li, C., Polling, M., Cao, L., et al. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.11.042 · Cited by 21
Supervised machine learning on Galactic filaments
Zavagno, A., Dupé, F.-X., Bensaid, S., et al. (2022) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202244103 · Cited by 21
Yang, J., Cai, M., Yang, X., & Zhou, Z. (2022) · Journal of Marine Science and Engineering · doi:10.3390/jmse10121841 · Cited by 23
Dimitriadis, A., Trivizakis, E., Papanikolaou, N. K., Tsiknakis, M., & Kostas Marias, A. (2022) · Insights into Imaging · doi:10.1186/s13244-022-01315-3 · Cited by 20
Efficient surface detection for assisting Collaborative Robots
Singh, S., Sajwan, M., Singh, G., Dixit, A. K., & Mehta, A. (2022) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2022.104339 · Cited by 20
Ultra-High-Resolution UAV-Based Detection of Alternaria solani Infections in Potato Fields
Vijver, R. V. D., Mertens, K. C., Heungens, K., Nuyttens, D. R. E., Wieme, J., Maes, W. H., Beek, J. V., Somers, B., & Saeys, W. (2022) · Remote Sensing · doi:10.3390/rs14246232 · Cited by 22
Dilated Convolution-based Feature Refinement Network for Crowd Localization
Gao, X., Xie, J., Chen, Z., et al. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3571134 · Cited by 21
A Bi-Party Engaged Modeling Framework for Renewable Power Predictions With Privacy-Preserving
Liu, H., & Zhang, Z. (2022) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2022.3224006 · Cited by 21
Design for Visitor Authentication Based on Face Recognition Technology Using CCTV
Mun, H.-J., & Lee, M.-H. (2022) · IEEE Access · doi:10.1109/access.2022.3223374 · Cited by 21
Murtaza, M., Sharif, M. I., Yasmin, M., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3218322 · Cited by 21
Gupta, M., Rajnish, K., & Bhattacharjee, V. (2022) · IEEE Access · doi:10.1109/access.2022.3213844 · Cited by 21
He, S., Yang, X., Jiang, C., Liang, G., Zhang, W., Pan, T., Wang, Q., Xu, F., Li, C., Liu, J., et al. (2022) · 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/cvpr52688.2022.02041 · Cited by 23
Towards Lifelong Federated Learning in Autonomous Mobile Robots with Continuous Sim-to-Real Transfer
Yu, X., Queralta, J. P., & Westerlund, T. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.10.123 · Cited by 26
Metasurface-Based Optical Analog Computing: From Fundamentals to Applications
Xu, D., Wen, S., & Luo, H. (2022) · Advanced Devices & Instrumentation · doi:10.34133/adi.0002 · Cited by 21
Noise Is Also Useful: Negative Correlation-Steered Latent Contrastive Learning
Yan, J., Luo, L., Xu, C., Deng, C. D., & Huang, H. (2022) · 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/cvpr52688.2022.00013 · Cited by 22
Li, L. K., Liang, Y., Shao, M., Lu, S., Liao, S., & Ouyang, D. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106482 · Cited by 22
Binary and Multi-class Classification of Brain Tumors using MRI Images
Gautam, S., Ahlawat, S., & Mittal, P. (2022) · International Journal of experimental research and review · doi:10.52756/ijerr.2022.v29.001 · Cited by 21
Source-free semi-supervised domain adaptation via progressive Mixup
Ma, N., Wang, H., Zhang, Z., Zhou, S., Chen, H., & Bu, J. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110208 · Cited by 23
BNET: Batch Normalization With Enhanced Linear Transformation
Xu, Y., Xie, L., Xie, C., Dai, W., Mei, J., Qiao, S., Shen, W., Xiong, H., & Yuille, A. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3235369 · Cited by 24
Semi-Self-Supervised Learning for Semantic Segmentation in Images with Dense Patterns
Najafian, K., Ghanbari, A., Kish, M. S., Eramian, M., Shirdel, G. H., Stavness, I., Jin, L., & Maleki, F. (2023) · Plant Phenomics · doi:10.34133/plantphenomics.0025 · Cited by 22
Adaptive Non-Maximum Suppression for improving performance of Rumex detection
Al-Badri, A. H., Ismail, N. A., Al-Dulaimi, K., Salman, G. A., & Salam, M. S. H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119634 · Cited by 24
PCG signal classification using a hybrid multi round transfer learning classifier
Ismail, S., & Ismail, B. (2023) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2023.01.004 · Cited by 21
Deep Learning-Based Video Action Recognition: A Review
Wani, M. H., & Faridi, A. R. (2022) · International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) · doi:10.1109/icccis56430.2022.10037736 · Cited by 25
Dynamic Mosaic algorithm for data augmentation
Li, Y., Cheng, R., Zhang, C., Chen, M., Liang, H., & Wang, Z. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023311 · Cited by 25
Yao, X., Yang, H., & Sheng, M. (2023) · Entropy · doi:10.3390/e25020318 · Cited by 21
Memory Clustering Autoencoder Method for Human Action Anomaly Detection on Surveillance Camera Video
Yan, M., Xiong, Y., & She, J. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3239219 · Cited by 20
Miyata, S., Kuwahara, Y., Tsunemoto, S., et al. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.112877 · Cited by 21
Yoshihara, S., Fukiage, T., & Nishida, S. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1047694 · Cited by 20
Tire Condition Monitoring Using Transfer Learning-Based Deep Neural Network Approach
Vasan, V., Sridharan, N. V., Anoop, P. S., & Sugumaran, V. (2023) · Sensors · doi:10.3390/s23042177 · Cited by 21
Multi-Modal Multi-Stage Underwater Side-Scan Sonar Target Recognition Based on Synthetic Images
Wang, J., Li, H., Huo, G., Li, C., & Wei, Y. (2023) · Remote Sensing · doi:10.3390/rs15051303 · Cited by 22
Shuffled-Xception-DarkNet-53: A content-based image retrieval model based on deep learning algorithm
Pathak, D., & Raju, U. S. N. (2023) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2023.108647 · Cited by 21
An interpretive constrained linear model for ResNet and MgNet
He, J., Xu, J., Zhang, L., & Zhu, J. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.03.011 · Cited by 24
Goldstein, J. A., Nateghi, R., Irmakci, I., & Cooper, L. (2023) · Placenta · doi:10.1016/j.placenta.2023.03.003 · Cited by 21
Yuan, S., Zhang, Z., Li, Q., Li, W., & Zhang, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3257342 · Cited by 21
Classification of tea leaves diseases by developed CNN, feature fusion, and classifier based model
YÜCEL, N., & Yıldırım, M. (2023) · International Journal of Applied Mathematics Electronics and Computers · doi:10.18100/ijamec.1235611 · Cited by 21
Artificial Intelligence in Symptomatic Carotid Plaque Detection: A Narrative Review
Miceli, G., Rizzo, G., Basso, M. G., Cocciola, E., Pennacchio, A. R., Pintus, C., & Tuttolomondo, A. (2023) · Applied Sciences · doi:10.3390/app13074321 · Cited by 24
Towards Fast Edge Detection Approach for Industrial Products
Honi, D. G., Ali, A. H., Abduljabbar, Z. A., et al. (2022) · doi:10.1109/iucc-cit-dsci-smartcns57392.2022.00046 · Cited by 21
Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation
Zheng, T., Wang, X., Feng, Z., Song, J., Hao, Y., Song, M., Wang, X., Wang, X., & Chen, C. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3265271 · Cited by 27
Pan, X., Wang, C., Yu, Y., Reljin, N., McManus, D. D., Darling, C. E., Chon, K. H., Mendelson, Y., & Lee, K. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102548 · Cited by 23
Face-based age estimation using improved Swin Transformer with attention-based convolution
Shi, C., Zhao, S., Zhang, K., Wang, Y., & Liang, L. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1136934 · Cited by 21
Chen, X., Yongchareon, S., & Knoche, M. (2023) · Journal of Smart Cities and Society · doi:10.3233/scs-230001 · Cited by 23
Efficient CUDA stream management for multi-DNN real-time inference on embedded GPUs
Pang, W., Luo, X., Chen, K., et al. (2023) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2023.102888 · Cited by 21
Meftah, A., Do, T. N., Kaddoum, G., Talhi, C., & Singh, S. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3271578 · Cited by 21
Wang, L., & Zhang, X. (2023) · Applied Sciences · doi:10.3390/app13095525 · Cited by 22
AugTarget Data Augmentation for Infrared Small Target Detection
Chen, S., Zhu, J., Ji, L., et al. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095060 · Cited by 21
Khishe, M., Azar, O. P., & Hashemzadeh, E. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15411-z · Cited by 21
YOLO for Penguin Detection and Counting Based on Remote Sensing Images
Wu, J., Xu, W., He, J., & Lan, M. (2023) · Remote Sensing · doi:10.3390/rs15102598 · Cited by 24
Fung, D. L. X., Li, X., Leung, C. K.-S., & Hu, P. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad059 · Cited by 21
Zhang, Y., Chen, C., Hu, R., & Yu, Y. (2023) · Remote Sensing · doi:10.3390/rs15123018 · Cited by 23
Adversarial self-attentive time-variant neural networks for multi-step time series forecasting
Gao, C., Zhang, N., Li, Y., Lin, Y. D., & Wan, H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120722 · Cited by 19
Advances in Computer-Aided Medical Image Processing
Cui, H., Hu, L. Q., & Chi, L. (2023) · Applied Sciences · doi:10.3390/app13127079 · Cited by 28
360 Degrees rumor detection: When explanations got some explaining to do
Janssens, B., Schetgen, L., Bogaert, M., Meire, M., & den Poel, D. V. (2023) · European Journal of Operational Research · doi:10.1016/j.ejor.2023.06.024 · Cited by 21
Dong, D., Ming, D., Weng, Q., Yang, Y., Fang, K., Xu, L. T., Du, T., Zhang, Y., & Liu, R. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3273726 · Cited by 22
Deep learning‐based panoptic segmentation: Recent advances and perspectives
Chuang, Y., Zhang, S., & Zhao, X. (2023) · IET Image Processing · doi:10.1049/ipr2.12853 · Cited by 25
Liu, Y., Pan, X., Liu, Q., & Li, G. (2023) · Land · doi:10.3390/land12051030 · Cited by 22
SpatialFormer: Semantic and Target Aware Attentions for Few-Shot Learning
Lai, J., Yang, S., Wu, W., et al. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i7.26016 · Cited by 21
Wei, G., Zhang, Z., Lan, C., Lü, Y., & Chen, Z. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i3.25376 · Cited by 34
Mistry, P. K., Strock, A., Liu, R., Young, G., & Menon, V. (2023) · Nature Communications · doi:10.1038/s41467-023-39548-5 · Cited by 23
SG-Det: Shuffle-GhostNet-Based Detector for Real-Time Maritime Object Detection in UAV Images
Zhang, L., Zhang, N., Shi, R., Wang, G., Xu, Y., & Chen, Z. (2023) · Remote Sensing · doi:10.3390/rs15133365 · Cited by 23
Machine learning paradigms in high-resolution remote sensing image interpretation
Zhou, P., Gong, C., Yao, X., & Junwei, H. (2021) · National Remote Sensing Bulletin · doi:10.11834/jrs.20210164 · Cited by 26
Transfer Learning Approach to Seed Taxonomy: A Wild Plant Case Study
Ibrahim, N. M., Gabr, D. G. I., Rahman, A., et al. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7030128 · Cited by 21
Epilepsy Detection with Multi-channel EEG Signals Utilizing AlexNet
Majzoub, S., Fahmy, A., Sibai, F. N., Diab, M. S., & Mahmoud, S. A. (2023) · Circuits Systems and Signal Processing · doi:10.1007/s00034-023-02423-1 · Cited by 22
Lee, G.-C., Li, J.-H., & Li, Z. (2023) · Applied Sciences · doi:10.3390/app13148132 · Cited by 25
OTFPF: Optimal transport based feature pyramid fusion network for brain age estimation
Fu, Y., Huang, Y., Zhang, Z., Dong, S., Xue, L., Niu, M., Li, Y., Shi, Z., Wang, Y., Zhang, H., et al. (2022) · Information Fusion · doi:10.1016/j.inffus.2023.101931 · Cited by 29
Forged document detection and writer identification through unsupervised deep learning approach
Tyagi, P., Agarwal, K., Jaiswal, G., Sharma, A., & Rani, R. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16146-7 · Cited by 21
Edge Guided GANs With Multi-Scale Contrastive Learning for Semantic Image Synthesis
Tang, H., Sun, G.-L., Sebe, N., & van Gool, L. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3298721 · Cited by 26
A Deep Convolutional Neural Network for Detecting Volcanic Thermal Anomalies from Satellite Images
Amato, E., Corradino, C., Torrisi, F., & Negro, C. D. (2023) · Remote Sensing · doi:10.3390/rs15153718 · Cited by 28
Xing, X., Liang, G., Wang, C., Jacobs, N., & Lin, A. (2023) · Bioengineering · doi:10.3390/bioengineering10080901 · Cited by 23
Teeth Lesion Detection Using Deep Learning and the Internet of Things Post-COVID-19
Shafi, I., Sajad, M., Fatima, A., et al. (2023) · Sensors · doi:10.3390/s23156837 · Cited by 21
Huang, Z., Li, K., Ke, C., Duan, H., Wang, M., & Bing, S. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106829 · Cited by 23
Streamflow Prediction in the Mekong River Basin Using Deep Neural Networks
Nguyen, T.-T.-H., Vu, D.-Q., Mai, S. T., & Dang, T. D. (2023) · IEEE Access · doi:10.1109/access.2023.3301153 · Cited by 22
Poyatos, J., Molina, D., Martínez-Seras, A., Ser, J. D., & Herrera, F. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110757 · Cited by 24
Ship detection in haze and low-light remote sensing images via colour balance and DCNN
Song, R., Li, T., & Li, T. (2023) · Applied Ocean Research · doi:10.1016/j.apor.2023.103702 · Cited by 21
Asymmetric Cascade Fusion Network for Building Extraction
Chan, S., Wang, Y., Lei, Y., Cheng, X., Chen, Z., & Wu, W. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3306018 · Cited by 22
Machine-Learning-Based Qubit Allocation for Error Reduction in Quantum Circuits
LeCompte, T., Qi, F., Yuan, X., Tzeng, N.-F., Najafi, M. H., & Peng, L. (2023) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2023.3301899 · Cited by 21
Jauhari, K., Rahman, A. Z., Huda, M. A., Widodo, A., & Prahasto, T. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02195-0 · Cited by 21
Huang, L.-Z., Li, Y., Tian, H.-B., Yang, Y., Li, X., Deng, W.-H., & Ye, J.-P. (2023) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52729.2023.00074 · Cited by 26
Masked Embedding Modeling With Rapid Domain Adjustment for Few-Shot Image Classification
Walsh, R., Osman, I. I., & Shehata, M. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3306916 · Cited by 24
Shen, W., Liu, Q. H., Ji, M., et al. (2023) · Sustainable Cities and Society · doi:10.1016/j.scs.2023.104909 · Cited by 21
Gao, L., Zhang, X., Tian, Y., Wang, B., & Li, J. (2023) · Electronics · doi:10.3390/electronics12173677 · 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
