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 45,901–46,000 of 108,439
Mapping Irrigated Croplands from Sentinel-2 Images Using Deep Convolutional Neural Networks
Li, W., Sun, Y., Zhou, Y., Gong, L., Li, Y., & Xin, Q. (2023) · Remote Sensing · doi:10.3390/rs15164071 · Cited by 15
Ravikumar, A., Sriraman, H., Saleena, B., & Prakash, B. (2023) · Health and Technology · doi:10.1007/s12553-023-00772-0 · Cited by 14
Zhang, Y., Zhang, J., Li, W., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13162714 · Cited by 13
Hong, Y., Kim, M. J., Lee, I., & Yoo, S. B. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3307285 · Cited by 13
Bafghi, R. A., & Gurari, D. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01560 · Cited by 17
Modified Deep-Convolution Neural Network Model for Flower Images Segmentation and Predictions
Jaiswal, V., Sharma, V., & Bisen, D. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16530-3 · Cited by 13
Peng, Q., Ge, S., Li, W., et al. (2023) · GIScience & Remote Sensing · doi:10.1080/15481603.2023.2249748 · Cited by 13
Wang, Y., Rahman, A., Duggar, W. N., Thomas, T. V., Roberts, P. R., Vijayakumar, S., Jiao, Z., Bian, L., & Wang, H. (2023) · Medical Physics · doi:10.1002/mp.16680 · Cited by 14
Chen, J., Wu, D., & Xie, R. (2023) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2200314 · Cited by 14
Research on Offloading Strategy of Twin UAVs Edge Computing Tasks for Emergency Communication
Ji, B., Zhang, M., Huang, J., Wang, Y., Xing, L., Li, T., Han, C., & Mumtaz, S. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3310486 · Cited by 12
Machine Learning for Self-Coherent Detection Short-Reach Optical Communications
Wu, Q., Xu, Z., Zhu, Y., et al. (2023) · Photonics · doi:10.3390/photonics10091001 · Cited by 13
Wang, C., He, J., Zhang, S., Ma, Z., & Wang, X. (2023) · Computers and Geotechnics · doi:10.1016/j.compgeo.2023.105772 · Cited by 16
Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking
Akhtar, M., Cocarascu, O., & Simperl, E. (2023) · Findings of the Association for Computational Linguistics: EACL 2023 · doi:10.18653/v1/2023.findings-eacl.30 · Cited by 18
Human shape representations are not an emergent property of learning to classify objects.
Malhotra, G., Dujmović, M., Hummel, J. E., & Bowers, J. S. (2023) · Journal of Experimental Psychology General · doi:10.1037/xge0001440 · Cited by 12
Wu, Q., Pei, Y., Cheng, Z., Hu, X., & Wang, C. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023773 · Cited by 13
Saad, O. M., Soliman, M. S., Chen, Y., et al. (2023) · Seismological Research Letters · doi:10.1785/0220230101 · Cited by 13
Ke, B., Lu, H., You, C., et al. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16383-w · Cited by 13
Hu, Y., Wang, J., Wang, X., et al. (2023) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2023.133263 · Cited by 13
Kartowisastro, I. H., & Latupapua, J. (2023) · Revue d intelligence artificielle · doi:10.18280/ria.370424 · Cited by 15
Caulley, D., Alemu, Y., Burson, S., Bautista, E. C., Tadesse, G. A., Kottmyer, C., Aeschbach, L., Cheungvivatpant, B., & Sezgın, E. (2023) · JMIR Research Protocols · doi:10.2196/51912 · Cited by 13
Characterizing and Modeling Mobile Networks User Traffic at Millisecond Level
Pérez, P. F., Fiandrino, C., & Widmer, J. (2023) · ACM International Workshop on Wireless Network Testbeds, Experimental Evaluation and Characterization (WiNTECH) · doi:10.1145/3615453.3616509 · Cited by 17
Peralta-Braz, P., Alamdari, M. M., Atroshchenko, E., & Hassan, M. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3309924 · Cited by 14
Using restored two-dimensional X-ray images to reconstruct the three-dimensional magnetopause
Wang, R., Wang, J., Li, D., Sun, T., Peng, X., & Guo, Y. (2023) · Earth and Planetary Physics · doi:10.26464/epp2023064 · Cited by 13
Yamati, F. R. I., Günder, M., Barreto, A., Bömer, J., Laufer, D., Bauckhage, C., & Mahlein, A. (2023) · Plant Disease · doi:10.1094/pdis-04-23-0779-re · Cited by 13
Dual-modality image feature fusion network for gastric precancerous lesions classification
Wang, J., Zhang, B., Wang, Y., Zhou, C., Zou, D., Vonsky, M. S., Mitrofanova, L. B., & Li, Q. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105516 · Cited by 13
Deep sample clustering domain adaptation for breast histopathology image classification
Wang, P., Yang, G., Li, Y., Li, P., Guo, Y., & Chen, R. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105500 · Cited by 15
Yang, Y., Zhang, L., Xu, G., Ren, G., & Wang, G. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121878 · Cited by 15
Xu, L., Kan, S., Yu, X., Liu, Y., Fu, Y., Peng, Y., Liang, Y., Cen, Y., Zhu, C., & Jiang, W. B. (2023) · iScience · doi:10.1016/j.isci.2023.108145 · Cited by 12
Liu, H., Wang, L., Zhang, Y., Lu, G., & Liu, L. (2023) · Physical review. E · doi:10.1103/physreve.108.045303 · Cited by 13
Lim, Y. G., Wu, J., Goh, Y. M., Tian, J., & Gan, V. J. L. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.105095 · Cited by 17
Communication Optimization Algorithms for Distributed Deep Learning Systems: A Survey
Yu, E., Dong, D., & Liao, X. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3323282 · Cited by 16
Manipulating Identical Filter Redundancy for Efficient Pruning on Deep and Complicated CNN
Hao, T., Ding, X., Han, J., Guo, Y., & Ding, G. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3298263 · Cited by 22
Combining uncertainty information with AI recommendations supports calibration with domain knowledge
Subramanian, H. V., Canfield, C., Shank, D. B., & Kinnison, M. (2023) · Journal of Risk Research · doi:10.1080/13669877.2023.2259406 · Cited by 16
On the effectiveness of handcrafted features for deepfake video detection
Kaddar, B., Fezza, S. A., Hamidouche, W., Akhtar, Z., Hadid, A., Serra-Sagrista, J. S., Hernández-Cabronero, M., & Sanchez, V. (2023) · Journal of Electronic Imaging · doi:10.1117/1.jei.32.5.053033 · Cited by 15
Indoor Obstacle Discovery on Reflective Ground via Monocular Camera
Xue, F., Chang, Y., Wang, T., Zhou, Y. C., & Ming, A. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01925-4 · Cited by 13
Pang, B., Zhao, S., & Liu, Y. (2023) · Remote Sensing · doi:10.3390/rs15205064 · Cited by 13
Pandey, S., Siddhu, L., & Panda, P. R. (2023) · ACM Transactions on Design Automation of Electronic Systems · doi:10.1145/3630012 · Cited by 14
Huang, Z., Zhang, Q., Shao, J., et al. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/ad060f · Cited by 13
Kaya, O., & Taşçı, B. (2023) · Diagnostics · doi:10.3390/diagnostics13213317 · Cited by 14
Image-based methods for dietary assessment: a survey
Zhang, S., Callaghan, V., & Che, Y. (2023) · Journal of Food Measurement & Characterization · doi:10.1007/s11694-023-02247-2 · Cited by 13
Variable Projection Support Vector Machines and Some Applications Using Adaptive Hermite Expansions
Dózsa, T., Deuschle, F., Cornelis, B., et al. (2023) · International Journal of Neural Systems · doi:10.1142/s0129065724500047 · Cited by 13
Iterative multitask learning and inference from seismic images
Gao, K. (2023) · Geophysical Journal International · doi:10.1093/gji/ggad424 · Cited by 13
DC Neural Networks avoid overfitting in one-dimensional nonlinear regression
Beltran‐Royo, C., Llopis-Ibor, L., Pantrigo, J. J., & Ramírez, I. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111154 · Cited by 13
How deep convolutional neural networks lose spatial information with training
Tomasini, U. M., Petrini, L., Cagnetta, F., & Wyart, M. (2023) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad092c · Cited by 19
Xiao, Q., Wang, Y., Wang, Y., et al. (2023) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2023.115807 · Cited by 13
Choi, K., Lee, Y., & Kim, C. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms242116013 · Cited by 15
Gwon, G.-H., Lee, J.-H., Kim, I., Baek, S.-C., & Jung, H. (2023) · Drones · doi:10.3390/drones7110666 · Cited by 14
LocMix: local saliency-based data augmentation for image classification
Yan, L., Ye, Y., Wang, C., & Sun, Y. (2023) · Signal Image and Video Processing · doi:10.1007/s11760-023-02852-0 · Cited by 13
Ueki, Y., Toyota, K., Ohira, T., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-46606-x · Cited by 13
Khaouane, A., Khaouane, L., Ferhat, S., & Hanini, S. (2023) · AAPS PharmSciTech · doi:10.1208/s12249-023-02686-6 · Cited by 13
LC-TTFS: Toward Lossless Network Conversion for Spiking Neural Networks With TTFS Coding
Qu, Y., Zhang, M., Wu, J., Tan, K. C., & Li, H. (2023) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2023.3334010 · Cited by 15
Chickpea disease classification using hybrid method
Abuhayi, B. M., & Bezabh, Y. A. (2023) · Smart Agricultural Technology · doi:10.1016/j.atech.2023.100371 · Cited by 13
Liu, P., Zhao, Y., Li, N., et al. (2023) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2023.107933 · Cited by 13
Infrared Object-Tracking Algorithm With Temporal Features Fusion Siamese Network
Song, J., Hao, X., Ren, G., & Miao, Q. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3335522 · Cited by 14
Wang, X., Tu, Y., Liu, J., Han, G., Yu, C., & Cui, J. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3338147 · Cited by 13
A NILM load identification method based on structured V-I mapping
Du, Z., Yin, B., Zhu, Y., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-48736-8 · Cited by 13
Extracting Citrus in Southern China (Guangxi Region) Based on the Improved DeepLabV3+ Network
Li, H., Jia, Z., Wang, J., et al. (2023) · Remote Sensing · doi:10.3390/rs15235614 · Cited by 13
Confidence-Based Similarity-Aware Personalized Federated Learning for Autonomous IoT
Han, X., Zhang, Q., He, Z., & Cai, Z. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3337520 · Cited by 14
Ajayi, O. G., & Olufade, O. O. (2023) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-x-1-w1-2023-991-2023 · Cited by 13
Appearance and Pose-guided Human Generation: A Survey
Liao, F., Zou, X., & Wong, W. K. (2023) · ACM Computing Surveys · doi:10.1145/3637060 · Cited by 16
Ren, Y., Zhu, D., Tong, K., Xv, L., Wang, Z., Kang, L., & Chai, J. (2023) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2023.101868 · Cited by 13
Self-Supervised Drivable Area Segmentation Using LiDAR's Depth Information for Autonomous Driving
Ma, F., Liu, Y., Wang, S., Wu, J., Qi, W., & Liu, M. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341687 · Cited by 13
Lin, H.-Y., Li, Y.-Q., & Lin, D.-T. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3345534 · Cited by 13
RRc-UNet 3D for lung tumor segmentation from CT scans of Non-Small Cell Lung Cancer patients
Le, V.-L., & Saut, O. (2023) · IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw60793.2023.00245 · Cited by 13
Ji, A., Xue, X., Zhang, L., Luo, X., & Man, Q. (2023) · Engineering Construction & Architectural Management · doi:10.1108/ecam-06-2023-0613 · Cited by 13
Intelligent Space Object Detection Driven by Data from Space Objects
Tang, Q., Li, X., Xie, M., & Zhen, J. (2023) · Applied Sciences · doi:10.3390/app14010333 · Cited by 13
Guo, Z., Wang, S., Zheng, Z., & Sun, K. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107822 · Cited by 13
Deep semantic learning for acoustic scene classification
Shao, Y., Ma, X., Ma, Y., & Zhang, W.-Q. (2024) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1186/s13636-023-00323-5 · Cited by 18
An augmentation aided concise CNN based architecture for COVID-19 diagnosis in real time
Kaur, B. P., Singh, H., Hans, R., Sharma, S. K., Kaushal, C., Hassan, M. M., & Shah, M. A. (2024) · Scientific Reports · doi:10.1038/s41598-024-51317-y · Cited by 13
Hubbard-Perez, M., Luchian, A., Milford, C., & Ressel, L. (2024) · Frontiers in Veterinary Science · doi:10.3389/fvets.2023.1309877 · Cited by 17
Two-stream vision transformer based multi-label recognition for TCM prescriptions construction
Zhao, Z., Qiang, Y., Yang, F., Hou, X., Zhao, J., & Song, K. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.107920 · Cited by 15
Lightweight Adaptive Feature De-Drifting for Compressed Image Classification
Peng, L., Cao, Y. H., Sun, Y., & Wang, Y. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3350917 · Cited by 16
Unsupervised Domain Adaptive Detection with Network Stability Analysis
Zhou, W., Fan, H., Luo, T., & Zhang, L. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00643 · Cited by 17
CTBANet: Convolution transformers and bidirectional attention for medical image segmentation
Luo, S., Pan, L., Jian, Y., Lu, Y., & Luo, S. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.01.018 · Cited by 13
A wearable three-axis force sensor based on deep learning technology for plantar measurement
Zhang, Q., Yang, R., Duan, Q., Zhao, Y., Qian, Z., Luo, D. M., Liu, Z., & Wang, R. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2023.148491 · Cited by 13
Deep learning-based siltation image recognition of water conveyance tunnels using underwater robot
Wu, X., & Li, J. (2024) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-023-00754-w · Cited by 14
Progressive pseudo-label framework for unsupervised hyperspectral change detection
Li, Q., Mu, T., Tuniyazi, A., Yang, Q., & Dai, H. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.103663 · Cited by 15
Wang, X., Dong, Y., Yang, J., Liu, Z.-P., & Lu, J.-S. (2024) · Frontiers of Environmental Science and Engineering · doi:10.1007/s11783-024-1814-5 · Cited by 10
Evolutionary channel pruning for real-time object detection
Yang, C., Lin, Z., Lan, Z., Chen, R., Wei, L., & Liu, Y. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111432 · Cited by 13
Hoang, N. Q., Kang, S., Yoon, H., et al. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.101874 · Cited by 13
Liuzzi, A. G., Meersmans, K., Peeters, R. R., Deyne, S. D., Dupont, P. J., & Vandenberghe, R. R. (2024) · Human Brain Mapping · doi:10.1002/hbm.26603 · Cited by 13
Luo, X., Zhang, H., Huang, X., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108090 · Cited by 13
Ju, Z., Ying, C., Ye, Z., Ma, D., Wang, B., & Cheng, Y. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.105970 · Cited by 13
Li, X., Ma, X., Zhao, Y., et al. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-00972-8 · Cited by 13
Automated identification of gastric cancer in endoscopic images by a deep learning model
Jasphin, C., & Geisa, J. M. (2024) · Automatika · doi:10.1080/00051144.2024.2304367 · Cited by 13
Transferable graph auto-encoders for cross-network node classification
Wu, H., Tian, L., Wu, Y., Zhang, J., Ng, M. K., & Long, J. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110334 · Cited by 13
Liu, C., Cai, Z., & Zhang, B. (2023) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct59356.2023.10419774 · Cited by 13
Enhanced YOLOv5s-Based Algorithm for Industrial Part Detection
Fang, Y., Wu, Q., Li, S., et al. (2024) · Sensors · doi:10.3390/s24041183 · Cited by 13
Learnable Gabor Kernels in Convolutional Neural Networks for Seismic Interpretation Tasks
Wang, F., & Alkhalifah, T. A. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3365910 · Cited by 13
Wu, Y., Xiang, Y., Tong, E., Ye, Y., Cui, Z., Tian, Y., Zhang, L., Liu, J., Han, Z., & Niu, W. (2024) · IEEE Network · doi:10.1109/mnet.2024.3366232 · Cited by 13
CycleGAN-Based Data Augmentation for Subgrade Disease Detection in GPR Images with YOLOv5
Yang, Y., Huang, L., Zhang, Z., Zhang, J., & Zhao, G. (2024) · Electronics · doi:10.3390/electronics13050830 · Cited by 16
Isaev, A., Dobroserdova, T. K., Danilov, A. A., et al. (2024) · Computation · doi:10.3390/computation12030041 · Cited by 13
Quantitative detection algorithm for deep-sea megabenthic organisms based on improved YOLOv5
Wang, W., Sun, Y. F., Gao, W. H., et al. (2024) · Frontiers in Marine Science · doi:10.3389/fmars.2024.1301024 · Cited by 15
Kim, M., Park, T., Oh, B. Y., et al. (2024) · Annals of Coloproctology · doi:10.3393/ac.2023.00892.0127 · Cited by 13
Deep learning CNN-APSO-LSSVM hybrid fusion model for feature optimization and gas-bearing prediction
Yang, J., Lin, N., Zhang, K., Cui, Y., Fu, C., & Zhang, D. (2024) · Petroleum Science · doi:10.1016/j.petsci.2024.02.012 · Cited by 19
A Robust CNN for Malware Classification against Executable Adversarial Attack
Zhang, Y., Jiang, J., Yi, C., et al. (2024) · Electronics · doi:10.3390/electronics13050989 · Cited by 13
Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective
Niño, S. B., Bernardino, J., & Domingues, I. (2024) · Sensors · doi:10.3390/s24061752 · Cited by 14
The classification of eye diseases from fundus images based on CNN and pretrained models
Benbakreti, S., Benbakreti, S., & Özkaya, U. (2024) · Acta Polytechnica · doi:10.14311/ap.2024.64.0001 · Cited by 13
Yosry, S., Elrefaei, L. A., ElKamaar, R., & Ziedan, R. R. (2024) · Discover Applied Sciences · doi:10.1007/s42452-024-05774-9 · Cited by 18
Diffractive neural networks with improved expressive power for gray-scale image classification
Zheng, M., Liu, W., Shi, L., & Zi, J. (2024) · Photonics Research · doi:10.1364/prj.513845 · Cited by 13
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
