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 44,301–44,400 of 108,439
Trejo-Chavez, O., Amézquita-Sánchez, J. P., Huerta-Rosales, J. R., Morales-Hernandez, L. A., Cruz-Albarrán, I. A., & Valtierra‐Rodriguez, M. (2022) · Electronics · doi:10.3390/electronics11233987 · Cited by 16
Learning to measure infrared properties of street views from visible images
Wang, L., Cheng, J., Song, J., Pan, X., & Zhang, C. (2022) · Measurement · doi:10.1016/j.measurement.2022.112320 · Cited by 15
GIN: Graph-Based Interaction-Aware Constraint Policy Optimization for Autonomous Driving
Yoo, S.-W., Kim, C., Choi, J., et al. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3227862 · Cited by 14
Aljabri, A. A., Alshanqiti, A. M., Alkhodre, A. B., Alzahem, A., & Hagag, A. (2022) · Optik · doi:10.1016/j.ijleo.2022.170408 · Cited by 15
Plasma image classification using cosine similarity constrained convolutional neural network
Falato, M. J., Wolfe, B. T., Natan, T., Zhang, X., Marshall, R. S., Zhou, Y., Bellan, P. M., & Wang, . (2022) · Journal of Plasma Physics · doi:10.1017/s0022377822000940 · Cited by 14
Preprocessing of Crack Recognition: Automatic Crack-Location Method Based on Deep Learning
Ren, R., Liu, F., Shi, P., Wang, H., & Huang, Y. (2022) · Journal of Materials in Civil Engineering · doi:10.1061/(asce)mt.1943-5533.0004605 · Cited by 15
Hou, Q., Xia, R., Zhang, J., Feng, Y., Zhan, Z., & Wang, X. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.103162 · Cited by 18
Assessing electrocardiogram changes after ischemic stroke with artificial intelligence
Zeng, Z., Wang, Q., Yu, Y., Zhang, Y., Chen, Q., Lou, W., Wang, Y., Yan, L., Cheng, Z., Xu, L., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0279706 · Cited by 17
Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentation
Zhan, A., Zhao, R., Pinto, L., Abbeel, P., & Laskin, M. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981055 · Cited by 68
Efficient Distributed DNNs in the Mobile-Edge-Cloud Continuum
Malandrino, F., Chiasserini, C. F., & di Giacomo, G. (2022) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2022.3222640 · Cited by 15
Feng, Y., Zhang, Y., Yang, P., Zhou, H., Du, H., & Li, X. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3210150 · Cited by 15
Evaluation of Pre-Trained CNN Models for Geographic Fake Image Detection
Fezza, S. A., Ouis, M. Y., Kaddar, B., Hamidouche, W., & Hadid, A. (2022) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp55362.2022.9949282 · Cited by 17
Enabling Variable High Spatial Resolution Retrieval From a Long Pulse BOTDA Sensor
Ge, Z., Shen, L., Zhao, C. H., Wu, H., Zhao, Z., & Tang, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3209674 · Cited by 14
A Study on Path Loss Modeling using ResNet and Pre-Training with Free Space Path Loss
Nagao, T., & Hayashi, T. (2022) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc54779.2022.9977760 · Cited by 14
Occupancy Prediction in Buildings: An approach leveraging LSTM and Federated Learning
Khan, I., Guerrieri, A., Spezzano, G., & Vinci, A. (2022) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc/picom/cbdcom/cy55231.2022.9927838 · Cited by 16
Tibau, X., Reimers, C., Gerhardus, A., Denzler, J., Eyring, V., & Runge, J. (2022) · Environmental Data Science · doi:10.1017/eds.2022.11 · Cited by 20
Kushwaha, D., Sharma, A., Gupta, N., Raj, R., Joshi, A., Mishra, J., Kohli, R., Miryala, S., Joshi, R., Dasgupta, S., et al. (2022) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas48785.2022.9937908 · Cited by 14
Multi-source Data Hiding in Neural Networks
Yang, Z., Wang, Z., Zhang, X., & Tang, Z. (2022) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp55362.2022.9948867 · Cited by 16
Kavzoğlu, T., & Yılmaz, E. Ö. (2022) · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · doi:10.55730/1300-0632.3929 · Cited by 17
Ghani, A., Hodeify, R. F., See, C. H., Keates, S., Lee, D.-J., & Bouridane, A. (2022) · Electronics · doi:10.3390/electronics11244234 · Cited by 14
QuCNN: A Quantum Convolutional Neural Network with Entanglement Based Backpropagation
Stein, S., Mao, Y., Ang, J., & Li, A. (2022) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec54971.2022.00054 · Cited by 16
An IoU-aware Siamese network for real-time visual tracking
Wei, B., Chen, H., Cao, S., Ding, Q., & Luo, H. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.01.041 · Cited by 13
Automatic Tuberculosis detection in cough patterns using NLP-style cough embeddings
Pahar, M., Theron, G., & Niesler, T. (2022) · International Conference on Engineering and Emerging Technologies (ICEET) · doi:10.1109/iceet56468.2022.10007261 · Cited by 14
Ichikawa, S., Itadani, H., & Sugimori, H. (2023) · Radiological Physics and Technology · doi:10.1007/s12194-023-00697-3 · Cited by 14
Mehta, P., Namuduri, S., Barbé, L., et al. (2023) · ECS Sensors Plus · doi:10.1149/2754-2726/acb2da · Cited by 14
Abdullah, M. Ü., & Alkan, A. (2022) · Traitement du signal · doi:10.18280/ts.390605 · Cited by 14
Ishimaru, M., Okada, Y., Uchiyama, R., Horiguchi, R., & Toyoshima, I. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20021588 · Cited by 14
Chen, S., Chen, M., Zhao, B., Mao, T., Wu, J., & Bao, W. (2023) · Remote Sensing · doi:10.3390/rs15030765 · Cited by 19
Appearance-Based Gaze Estimation Method Using Static Transformer Temporal Differential Network
Li, Y., Huang, L., Chen, J., Wang, X., & Tan, B. (2023) · Mathematics · doi:10.3390/math11030686 · Cited by 15
BoxMask: Revisiting Bounding Box Supervision for Video Object Detection
Hashmi, K. A., Pagani, A., Stricker, D., & Afzal, M. Z. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00207 · Cited by 16
Leaf-Counting in Monocot Plants Using Deep Regression Models
Xie, X., Ge, Y., Walia, H., Yang, J., & Yu, H. (2023) · Sensors · doi:10.3390/s23041890 · Cited by 18
Applications of Deep Learning to Neurodevelopment in Pediatric Imaging: Achievements and Challenges
Hu, M., Nardi, C., Zhang, H., & Ang, K. K. (2023) · Applied Sciences · doi:10.3390/app13042302 · Cited by 14
Zhao, M. M., Cheng, Y., Qin, X., et al. (2023) · Sensors · doi:10.3390/s23042109 · Cited by 14
SSAD: Single-Shot Multi-Scale Attentive Detector for Autonomous Driving
Chintakindi, B. M., & Hashmi, M. F. (2023) · IETE Technical Review · doi:10.1080/02564602.2023.2176932 · Cited by 14
Deep Hashing With Multi-Central Ranking Loss for Multi-Label Image Retrieval
Cui, C., Huo, H., & Fang, T. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3244516 · Cited by 15
IAACS: Image aesthetic assessment through color composition and space formation
Yang, B., Zhu, C., Li, F. W. B., Wei, T., Liang, X., & Wang, Q. (2023) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2022.06.006 · Cited by 19
Frame-Recurrent Video Crowd Counting
Hou, Y., Zhang, S., Ma, R., Jia, H., & Xie, X. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3250946 · Cited by 16
Cai, L., Wei, X., Qing, Y., Lu, M., Yi, G., Wang, J., & Dong, Y. (2023) · Cognitive Neurodynamics · doi:10.1007/s11571-023-09944-0 · Cited by 14
Chakraborty, D., Zhuang, Z., Xue, H., Fiecas, M. J. A., Shen, X., Initiative, A. D. N., & Pan, W. (2023) · Genes · doi:10.3390/genes14030626 · Cited by 15
IoT Based Motion Detector Using Raspberry Pi Gadgetry
Idoko, B., Idoko, J. B., Kazaure, Y. Z. M., et al. (2022) · doi:10.1109/ited56637.2022.10051334 · Cited by 14
Choi, I., Ko, K.-E., Song, H., Jung, B.-J., Hwang, J.-H., Moon, H., & Yang, G. (2023) · Applied Sciences · doi:10.3390/app13053216 · Cited by 14
Zhou, Z., Yuan, H., & Cai, X. (2023) · Mathematics · doi:10.3390/math11051245 · Cited by 15
CAM-CAN: Class activation map-based categorical adversarial network
Batchuluun, G., Choi, J., & Park, K. R. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119809 · Cited by 14
SuperDet: An Efficient Single-Shot Network for Vehicle Detection in Remote Sensing Images
Ju, M., Niu, B., Jin, S., & Liu, Z. (2023) · Electronics · doi:10.3390/electronics12061312 · Cited by 14
Classification of External Vibration Sources through Data-Driven Models Using Hybrid CNNs and LSTMs
Liang, R., Liu, W., Kaewunruen, S., Zhang, H., & Wu, Z. (2023) · Structural Control and Health Monitoring · doi:10.1155/2023/1900447 · Cited by 15
Federated Learning for Vision-based Obstacle Avoidance in the Internet of Robotic Things
Yu, X., Queralta, J. P., & Westerlund, T. (2022) · International Conference on Fog and Mobile Edge Computing (FMEC) · doi:10.1109/fmec57183.2022.10062657 · Cited by 16
Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification
Tsai, M.-J., Lee, Y.-C., & Chen, T.-M. (2023) · Algorithms · doi:10.3390/a16030160 · Cited by 19
A Novel Brain Tumor Classification Model Using Machine Learning Techniques
Pal, S. S., Raymahapatra, P., Paul, S., Dolui, S., Chaudhuri, A. K., & Das, S. (2023) · International Journal of Engineering Technology and Management Sciences · doi:10.46647/ijetms.2023.v07i02.011 · Cited by 14
Using convolutional neural networks for acoustic-based emergency vehicle detection
Анатольевич, Л. А., Зайдакбаевич, К. А., & Panishev, S. A. (2023) · Modern Transportation Systems and Technologies · doi:10.17816/transsyst20239195-107 · Cited by 16
RMAML: Riemannian meta-learning with orthogonality constraints
Tabealhojeh, H., Adibi, P., Karshenas, H., Roy, S. K., & Harandi, M. T. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109563 · Cited by 14
Efficient Machine Learning on Edge Computing Through Data Compression Techniques
Larrakoetxea, N. G., Eskubi-Astobiza, J., Pastor-López, I., Sanz, B., García-Barruetabeña, J., & Rego, A. Z. (2023) · IEEE Access · doi:10.1109/access.2023.3263391 · Cited by 14
He, Y., Guo, Z., Wang, X., & Waheed, A. (2023) · Energies · doi:10.3390/en16073170 · Cited by 15
Li, S., Liu, C., & Chen, J. (2023) · Minerals · doi:10.3390/min13040504 · Cited by 14
Wang, Y., Liu, J., Xu, L., Yu, F., & Zhang, S. (2023) · Water · doi:10.3390/w15071422 · Cited by 14
A survey on molecular-scale learning systems with relevance to DNA computing
Nagipogu, R. T., Fu, D., & Reif, J. H. (2023) · Nanoscale · doi:10.1039/d2nr06202j · Cited by 15
A Model for Infrastructure Detection along Highways Based on Remote Sensing Images from UAVs
Jiang, X., Cui, Q., Wang, C., et al. (2023) · Sensors · doi:10.3390/s23083847 · Cited by 14
Campus Abnormal Behavior Recognition With Temporal Segment Transformers
Liu, H. L., Chuah, J. H., Khairuddin, A. S. M., Zhao, X. M., & Wang, X. D. (2023) · IEEE Access · doi:10.1109/access.2023.3266440 · Cited by 14
Deep Active Learning for Automatic Mitotic Cell Detection on HEp-2 Specimen Medical Images
Anaam, A., Al–antari, M. A., Hussain, J., Samee, N. M. A., Alabdulhafith, M. I., & Gofuku, A. (2023) · Diagnostics · doi:10.3390/diagnostics13081416 · Cited by 15
Disease Classification in Bell Pepper Plants Based on Deep Learning Network Architecture
Mathew, M. P., Elayidom, M. S., & Jagathyraj, V. P. (2023) · doi:10.1109/inocon57975.2023.10101269 · Cited by 14
Bracci, S., Mraz, J., Zeman, A. A., Leys, G., & de Beeck, H. O. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011086 · Cited by 18
Park, S., & You, C. (2023) · Applied Sciences · doi:10.3390/app13095507 · Cited by 17
Burrai, G. P., Gabrieli, A., Polinas, M., Murgia, C., Becchere, M. P., Demontis, P., & Antuofermo, E. (2023) · Animals · doi:10.3390/ani13091563 · Cited by 21
DC3DCD: Unsupervised learning for multiclass 3D point cloud change detection
de Gélis, I., Lefèvre, S., & Corpetti, T. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.10.022 · Cited by 16
Convolutional block attention based network for copy-move image forgery detection
Sabeena, M., & Abraham, L. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15649-7 · Cited by 15
A multi-subpopulation genetic algorithm-based CNN approach for ceramic tile defects classification
Huynh, N.-T. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02130-3 · Cited by 14
Detection of Cattle Key Parts Based on the Improved Yolov5 Algorithm
Shao, D., He, Z., Fan, H., et al. (2023) · Agriculture · doi:10.3390/agriculture13061110 · Cited by 14
Boosting Adversarial Transferability with Shallow-Feature Attack on SAR Images
Lin, G., Pan, Z., Zhou, X., Duan, Y., Bai, W., Zhan, D., Zhu, L., Zhao, G., & Li, T. (2023) · Remote Sensing · doi:10.3390/rs15102699 · Cited by 16
Zhang, K., Fu, W., Zhang, J., Zhou, W., Liu, C., Liu, P., Zhang, L., Yan, X., Yang, Y., Sun, H. X., et al. (2023) · SPE Journal · doi:10.2118/215821-pa · Cited by 16
Chaurasia, N., Jha, S. K., & Sangal, S. (2023) · Materialia · doi:10.1016/j.mtla.2023.101803 · Cited by 14
NDT Method for Weld Defects Based on FMPVit Transformer Model
Liu, Y., Yuan, K., Li, T., & Li, S. (2023) · IEEE Access · doi:10.1109/access.2023.3283589 · Cited by 14
Shao, J., Pan, T., Fan, L., Li, Z., Yang, J., Zhang, S., Zhang, J., Chen, D., Zhu, X., Chen, H., et al. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105048 · Cited by 15
HA-Transformer: Harmonious aggregation from local to global for object detection
Chen, Y., Chen, S., Deng, Y., & Wang, K. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120539 · Cited by 14
Chromosome Detection in Metaphase Cell Images Using Morphological Priors
Wang, J., Zhou, C., Chen, S., Hu, J., Wu, M., Jiang, X., Xu, C., & Qian, D. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3286572 · Cited by 14
FireClassNet: a deep convolutional neural network approach for PJF fire images classification
Daoud, Z., Hamida, A. B., & Amar, C. B. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08750-3 · Cited by 14
Zeng, Z., Huang, C., Zhu, W., Wen, Z., & Yuan, X. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023619 · Cited by 14
Yiwen, 胡. H., Xin, 刘. L., Cuifang, 匡. K., Xu, 刘. L., & Hao, X. (2023) · Chinese Journal of Lasers · doi:10.3788/cjl230470 · Cited by 14
Seismic Stratigraphic Interpretation Based on Deep Active Learning
Gu, X., Lu, W., Ao, Y., et al. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3288737 · Cited by 14
Physics-added neural networks: An image-based deep learning for material printing system
Kwon, S. W., Kim, J. S., Lee, H. M., & Lee, J. S. (2023) · Additive manufacturing · doi:10.1016/j.addma.2023.103668 · Cited by 14
Genome-wide scans for selective sweeps using convolutional neural networks
Zhao, H., Souilljee, M., Pavlidis, P., & Alachiotis, N. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad265 · Cited by 14
Classifying diabetic macular edema grades using extended power of deep learning
Kumar, A., & Tewari, A. S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15746-7 · Cited by 14
Zhang, Y., Tsang, M. K. C., Knackstedt, M. A., et al. (2023) · Journal of Rock Mechanics and Geotechnical Engineering · doi:10.1016/j.jrmge.2023.04.001 · Cited by 14
Accelerated American option pricing with deep neural networks
Anderson, D. P., & Ulrych, U. (2023) · Quantitative Finance and Economics · doi:10.3934/qfe.2023011 · Cited by 14
An Optimal Transport View of Class-Imbalanced Visual Recognition
Jin, L., Lang, D., & Lei, N. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01831-9 · Cited by 14
CLIP2GAN: Toward Bridging Text With the Latent Space of GANs
Wang, Y., Zhou, W., Bao, J., Wang, W., Li, L., & Li, H. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3294261 · Cited by 16
Hu, M., Wu, B., Lu, D., Xie, J., Chen, Y., Yang, Z., & Dai, W. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1221453 · Cited by 15
MFF-YOLO: An Accurate Model for Detecting Tunnel Defects Based on Multi-Scale Feature Fusion
Жу, А., Wang, B., Xie, J., & Ma, C. (2023) · Sensors · doi:10.3390/s23146490 · Cited by 14
Subbarayudu, Y., & Sureshbabu, A. (2023) · Ingénierie des systèmes d information · doi:10.18280/isi.280317 · Cited by 14
Experimenting with Extreme Learning Machine for Biomedical Image Classification
Mercaldo, F., Brunese, L., Martinelli, F., Santone, A., & Cesarelli, M. (2023) · Applied Sciences · doi:10.3390/app13148558 · Cited by 23
Zubler, F., & Tzovara, A. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1183810 · Cited by 18
Bose, P., Rana, P., & Ghosh, P. (2023) · IEEE Access · doi:10.1109/access.2023.3299877 · Cited by 20
Sadras, N., Sani, O. G., Ahmadipour, P., & Shanechi, M. M. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/acec14 · Cited by 17
Facial Expression Recognition Based on Fine-Tuned Channel–Spatial Attention Transformer
Yao, H., Yang, X., Chen, D., Wang, Z., & Tian, Y. (2023) · Sensors · doi:10.3390/s23156799 · Cited by 17
Deep convolutional neural network for rib fracture recognition on chest radiographs
Huang, S.-T., Liu, L.-R., Chiu, H., Huang, M.-Y., & Tsai, M.-F. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1178798 · Cited by 16
Unsupervised anomaly appraisal of cleft faces using a StyleGAN2-based model adaptation technique
Hayajneh, A., Shaqfeh, M. O., Serpedin, E., & Stotland, M. A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0288228 · Cited by 17
Shrimpseed_Net: Counting of Shrimp Seed Using Deep Learning on Smartphones for Aquaculture
Liu, D., Xu, B., Cheng, Y., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3302249 · Cited by 14
Xu, C., Liu, W., Zheng, Y., Wang, S., & Chang, C.-H. (2023) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2023.3298802 · Cited by 16
Nail Disease Prediction using a Deep Learning Integrated Framework
Marulkar, S., & Narain, B. (2023) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit59222.2023.10205721 · Cited by 16
Huang, H., Du, R., Wang, Z., Li, X., & Yuan, G. (2023) · Sensors · doi:10.3390/s23167084 · Cited by 13
FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation
Gu, H., Luo, J., Kang, Y., Fan, L., & Yang, Q. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/418 · Cited by 22
VideoMatt: A Simple Baseline for Accessible Real-Time Video Matting
Li, J., Ohanyan, M., Goel, V., et al. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00211 · Cited by 14
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
