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 63,401–63,500 of 108,435
Cross-view hashing via supervised deep discrete matrix factorization
Xiong, Y., Xu, Y. L., & Shu, X. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107270 · Cited by 6
Skin Anomaly Detection Using Classification Algorithms
ANDRONESCU, A.-D., Năstac, I., & Ţiplica, G. (2019) · IEEE International Symposium for Design and Technology in Electronic Packaging (SIITME) · doi:10.1109/siitme47687.2019.8990764 · Cited by 6
Performance Analysis of Pre-trained Deep Learning Networks for Brain Tumor Categorization
Rahmathunneesa, A. P., & Muneer, K. V. A. (2019) · doi:10.1109/icacc48162.2019.8986151 · Cited by 6
Vehicle Face Detection Based on Cascaded Convolutional Neural Networks
Jing, S., Hu, C., Wang, C., et al. (2019) · Chinese Automation Congress · doi:10.1109/cac48633.2019.8996162 · Cited by 6
NetVision: On-Demand Video Processing in Wireless Networks
Lu, Z., Chan, K., Urgaonkar, R., et al. (2019) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2019.2954909 · Cited by 6
Research on Image Caption Method Based on Mixed Image Features
Zhenyu, Y., & Zhang, J. (2019) · IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) · doi:10.1109/iaeac47372.2019.8998010 · Cited by 6
Convolutional Classification of Pathogenicity in H5 Avian Influenza Strains
Chadha, A., Dara, R. A., & Poljak, Z. (2019) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2019.00259 · Cited by 6
Forcing Interpretability for Deep Neural Networks through Rule-Based Regularization
Burkart, N., Huber, M. F., & Faller, P. (2019) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2019.00126 · Cited by 9
Remote Sensing Object Localization with Deep Heterogeneous Superpixel Features
Yang, A., Hurt, J. A., Veal, C., & Scott, G. J. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006120 · Cited by 6
Aiming AI at a moving target: health (or disease)
Nadin, M. (2020) · AI & Society · doi:10.1007/s00146-020-00943-x · Cited by 11
Efficient dynamic domain adaptation on deep CNN
Yang, Z., Liu, G., Xie, X., et al. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08584-z · Cited by 6
Pedestrian and Vehicle detection in automotive embedded systems using deep neural networks
Kaur, P., & Sobti, R. (2018) · International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE) · doi:10.1109/icrieece44171.2018.9008972 · Cited by 6
Performance Evaluation of Deep Autoencoder Network for Speech Emotion Recognition
AndleebSiddiqui, M., Hussain, W., Abbas, S., et al. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110276 · Cited by 6
Looking deeper into Time for Activities of Daily Living Recognition
Das, S., Thonnat, M., & Brémond, F. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093575 · Cited by 8
Abbas, W., Masip, D., & Giovannucci, A. (2020) · IEEE Access · doi:10.1109/access.2020.2975926 · Cited by 5
Tu, Y., Wang, M., & Wang, S. (2019) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps45667.2019.9024610 · Cited by 6
Evaluation of Feature Extraction Methods for Classification of Palm Trees in UAV Images
Chen, Z. Y., & Liao, I. Y. (2019) · International Conference on Computer and Drone Applications (IConDA) · doi:10.1109/iconda47345.2019.9034913 · Cited by 6
Ahmad, M. B. B., Chowdhury, M. R. J., Ahmed, A., et al. (2020) · International Conference on Computing Advancements (ICCA) · doi:10.1145/3377049.3377118 · Cited by 6
TM‐Link: An Internationally Linked Trademark Database
Petrie, S., Adams, M., Mitra‐Kahn, B., et al. (2020) · Australian Economic Review · doi:10.1111/1467-8462.12373 · Cited by 6
Monocular VO Based on Deep Siamese Convolutional Neural Network
Wang, H., Ban, X., Ding, F., Xiao, Y., & Zhou, J. (2020) · Complexity · doi:10.1155/2020/6367273 · Cited by 6
Distilling the knowledge in CNN for WCE screening tool
Garbay, T., Chuquimia, O., Pinna, A., Sahbi, H., Dray, X., & Granado, B. (2019) · Conference on Design and Architectures for Signal and Image Processing · doi:10.1109/dasip48288.2019.9049201 · Cited by 7
Different Approaches for Solving Inverse Seismic Problems in Fractured Media
Golubev, V. I., Muratov, M. V., & Petrov, I. B. (2020) · Smart innovation, systems and technologies · doi:10.1007/978-981-15-2600-8_15 · Cited by 6
Deep Neural Network Based Multiple Targets DOA Estimation for Millimeter-Wave Radar
Tang, G., Gao, X., Chen, Z., Zhang, Y., Zhong, H., & Li, M. (2019) · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00117 · Cited by 6
Object detection for KRSBI robot soccer using PeleeNet on omnidirectional camera
Winarno, W., Agoes, A. S., Agustin, E. I., et al. (2020) · TELKOMNIKA (Telecommunication Computing Electronics and Control) · doi:10.12928/telkomnika.v18i4.15009 · Cited by 6
Afshar, P., Oikonomou, A., Plataniotis, K. N., & Mohammadi, A. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053243 · Cited by 6
Fast and Accurate Embedded DCNN for Rgb-D Based Sign Language Recognition
Wang, C.-C., Chiu, C., Huang, C.-T., et al. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054076 · Cited by 6
Spatial-temporal dual-actor CNN for human interaction prediction in video
Afrasiabi, M., Khotanlou, H., & Gevers, T. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08845-2 · Cited by 6
Minimal Filtering Algorithms for Convolutional Neural Networks
Cariow, A., & Cariowa, G. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-74556-1_5 · Cited by 6
Activity Monitoring for ICU Patients Using Deep Learning and Image Processing
Magi, N., & Prasad, B. G. (2020) · SN Computer Science · doi:10.1007/s42979-020-00147-6 · Cited by 6
Sheet, S., Ghosh, A., Ghosh, R., & Chakrabarti, A. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.03.341 · Cited by 6
Su, X., Liu, H., & Tao, L. (2020) · Applied Sciences · doi:10.3390/app10082932 · Cited by 6
Khan, M. Z., Hassan, S. U., Draz, H. U., Khan, M. U. G., & Iqbal, R. (2020) · Unmanned Aerial Vehicles in Smart Cities · doi:10.1007/978-3-030-38712-9_5 · Cited by 4
Video object tracking and segmentation with box annotation
Wang, Y., Choi, J., Zhang, K., Huang, Q., Chen, Y., Lee, M., & Kuo, C. ‐. J. (2020) · Signal Processing Image Communication · doi:10.1016/j.image.2020.115858 · Cited by 6
Intelligent Characterization System for Char Combustion Reactivity using Deep Learning
Alpana, A., Chand, S., & Mishra, V. (2020) · International Conference on Signal Processing and Integrated Networks (SPIN) · doi:10.1109/spin48934.2020.9070848 · Cited by 6
Learning Interpretable Hidden State Structures for Handwritten Numeral Recognition
Susan, S., & Malhotra, J. (2020) · International Conference on Computational Intelligence and Networks (CINE) · doi:10.1109/cine48825.2020.234394 · Cited by 6
Jiao, Y., Schneider, B. S. P., Regentova, E. E., & Yang, M. (2019) · Journal of medical imaging · doi:10.1117/1.jmi.6.2.024006 · Cited by 6
A new approach to image classification based on a deep multiclass AdaBoosting ensemble
Asil, H., & Bagherzadeh, J. (2020) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v10i5.pp4872-4880 · Cited by 6
Object Detection and Danger Warning of Transmission Channel Based on Improved YOLO Network
Ning, W., Mu, X., Zhang, C., Dai, T., Qian, S., & Sun, X. (2020) · IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · doi:10.1109/itnec48623.2020.9085177 · Cited by 6
Masked Face Detection Based on Locally Nonlinear Feature Fusion
Peng, X., Cao, J., & Zhang, F. (2020) · International Conference on Software and Computer Applications (ICSCA) · doi:10.1145/3384544.3384568 · Cited by 6
Visual Relationship Detection with Contextual Information
Wang, Y. L. Y., & Zhu, Y. (2020) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2020.07451 · Cited by 6
Useche, P., Moreno, R. J., & Baquero, J. E. M. (2020) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v10i5.pp4712-4720 · Cited by 6
Skrimponis, P., Pissadakis, E., Alachiotis, N., et al. (2020) · Advances in parallel computing · doi:10.3233/apc200099 · Cited by 6
Random Patterns Clothing Image Retrieval using Convolutional Neural Network
Shah, R., Bhatti, N., Akhtar, N., et al. (2020) · doi:10.1109/icetst49965.2020.9080687 · Cited by 6
Obstacle Detection Using Faster R-CNN Oriented to an Autonomous Feeding Assistance System
Pinzon-Arenas, J. O., & Moreno, R. J. (2020) · International Conference on Information and Computer Technologies (ICICT) · doi:10.1109/icict50521.2020.00029 · Cited by 6
Automatic segmentation of head-neck organs by Multi-mode CNNs for radiation therapy
Yang, Q., Zhang, S., Sun, X., Sun, J., & Yuan, K. (2019) · IEEE International Conference on Medical Imaging Physics and Engineering (ICMIPE) · doi:10.1109/icmipe47306.2019.9098166 · Cited by 6
Weeping and Gnashing of Teeth: Teaching Deep Learning in Image and Video Processing Classes
Bovik, A. C. (2020) · IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) · doi:10.1109/ssiai49293.2020.9094606 · Cited by 6
Learning lithography hotspot detection from ImageNet
Xiao, Y., & Huang, X. (2019) · IEEE International Conference on Electronic Measurement & Instruments · doi:10.1109/icemi46757.2019.9101673 · Cited by 6
Traffic sign classification network using inception module
Dongfang, Z., Wenjing, K., Li, T., & Gong-liang, L. (2019) · IEEE International Conference on Electronic Measurement & Instruments · doi:10.1109/icemi46757.2019.9101433 · Cited by 6
Bidirectional Broad Learning System
Huang, P., & Chen, B. (2020) · IEEE International Conference on Industrial Engineering and Applications (ICIEA) · doi:10.1109/iciea49774.2020.9102007 · Cited by 7
Pre-trained CNNs as Visual Feature Extractors: A Broad Evaluation
Holliday, A. G. C., & Dudek, G. (2020) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv50864.2020.00019 · Cited by 9
YOLO-mini-tiger: Amur Tiger Detection
Wei, R., He, N., & Lü, K. (2020) · International Conference on Multimedia Retrieval · doi:10.1145/3372278.3390710 · Cited by 6
RRNet: Repetition-Reduction Network for Energy Efficient Depth Estimation
Oh, S., Kim, H.-J. S., Lee, J., & Kim, J. (2020) · IEEE Access · doi:10.1109/access.2020.3000773 · Cited by 6
ADMMiRNN: Training RNN with Stable Convergence via an Efficient ADMM Approach
Tang, Y. H., Kan, Z., Sun, D., Qiao, L., Xiao, J., Lai, Z., & Li, D. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-67661-2_1 · Cited by 9
Nested Scale-Editing for Conditional Image Synthesis
Zhang, L., Wang, J., Xu, Y., Min, J., Wen, T., Gee, J. C., & Shi, J. (2020) · 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/cvpr42600.2020.00552 · Cited by 5
Deep Restoration of Invisible QR Code from TPVM Display
Song, K., Liu, N., Gao, Z., et al. (2020) · IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew46912.2020.9105961 · Cited by 6
Direct Quantization for Training Highly Accurate Low Bit-width Deep Neural Networks
Hoang, T., Do, T.-T., Nguyen, T., & Cheung, N. M. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/292 · Cited by 8
Musculoskeletal Abnormality Detection in Humerus Radiographs Using Deep Learning
Chawla, N., & Kapoor, N. (2020) · Revue d intelligence artificielle · doi:10.18280/ria.340212 · Cited by 6
Testa, E., Noor, S. L., Zografos, O., et al. (2020) · Design, Automation, and Test in Europe · doi:10.23919/date48585.2020.9116490 · Cited by 6
Operation Mode Decision of Indoor Cleaning Robot Based on Causal Reasoning and Attribute Learning
Li, Y., Zhang, D., Yin, F., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3003343 · Cited by 6
Diagnosis of Leaf Disease Using Enhanced Convolutional Neural Network
Begum, A. S., Savitha, S., Shahila, S., et al. (2020) · International Journal of Innovative Research in Applied Sciences and Engineering · doi:10.29027/ijirase.v3.i12.2020.579-586 · Cited by 6
Zhang, T., Hou, M., Zhou, T. Y., et al. (2020) · Computer Science and Information Systems · doi:10.2298/csis191222010z · Cited by 6
Svatiuk, D., Сватюк, О., & Белей, О. І. (2020) · Cybersecurity Education Science Technique · doi:10.28925/2663-4023.2020.8.97112 · Cited by 6
A Deep Learning Approach to Automatic Recognition of Arcus Senilis
Amini, N., & Ameri, A. (2020) · Journal of Biomedical Physics and Engineering · doi:10.31661/jbpe.v0i0.2003-1080 · Cited by 6
Semi-supervised Classification of PolSAR Image Based on Self-training Convolutional Neural Network
Qin, X., Yu, W., Wang, P., Chen, T., & Zou, H. (2020) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-3947-3_31 · Cited by 6
Li, P., Li, J., Chen, Y., et al. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-020-03367-y · Cited by 6
Streamlining Choice of CNNs and Structure Framing of Convolution Layer
Dahiya, S., Tyagi, R., & Gaba, N. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-4032-5_64 · Cited by 6
Lu, H., Lou, Y.-T., Jin, B., et al. (2020) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2020.011270 · Cited by 6
Face Liveness Detection Based on Parallel CNN
Li, X., Wu, W., Li, T., et al. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1549/4/042069 · Cited by 6
Intermediate level cortical areas and the multiple roles of area V4
Parker, A. J. (2020) · Current Opinion in Physiology · doi:10.1016/j.cophys.2020.07.003 · Cited by 6
Feinberg, B., Heyman, B. C., Mikhailenko, D., et al. (2020) · ACM/IEEE International Symposium on Computer Architecture (ISCA) · doi:10.1109/isca45697.2020.00091 · Cited by 6
Han, W., Johnson, C., Warner, A., Gaed, M., Gómez, J. A., Moussa, M., Chin, J. L., Pautler, S. E., Bauman, G. S., & Ward, A. D. (2020) · Journal of medical imaging · doi:10.1117/1.jmi.7.4.047501 · Cited by 7
Machine Learning Techniques to Support Many-Core Resource Management: Challenges and Opportunities
Rapp, M., Amrouch, H., Wolf, M. C., et al. (2019) · ACM/IEEE Workshop on Machine Learning for CAD (MLCAD) · doi:10.1109/mlcad48534.2019.9142064 · Cited by 6
Dey, S., Saha, S., Singh, A. K., et al. (2020) · IEEE India Council International Conference (INDICON) · doi:10.1109/indicon49873.2020.9342108 · Cited by 6
A Virtual End-to-End Learning System for Robot Navigation Based on Temporal Dependencies
Zhang, Y., Ge, R., Lyu, L., Zhang, J., Lyu, C., & Yang, X. (2020) · IEEE Access · doi:10.1109/access.2020.3010695 · Cited by 6
A software‐defined radio testbed for deep learning‐based automatic modulation classification
Sowjanya, P. S., & Satyanarayana, P. (2020) · International Journal of Communication Systems · doi:10.1002/dac.4556 · Cited by 6
Filter pruning by image channel reduction in pre-trained convolutional neural networks
Chung, G. S., & Won, C. S. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09373-9 · Cited by 6
A New Loss Function for Traffic Classification Task on Dramatic Imbalanced Datasets
Xu, L., Zhou, X., Lin, X., Ren, Y., Qin, Y., & Liu, J. (2020) · International Conference on Communications · doi:10.1109/icc40277.2020.9148638 · Cited by 6
CycleGAN-Based Deblurring for Gaze Tracking in Vehicle Environments
Yoon, H. S., & Park, K. R. (2020) · IEEE Access · doi:10.1109/access.2020.3012191 · Cited by 7
Ship Image Classification Using Deep Learning Method
Patil, P., Ranganathan, M., & Patil, H. Y. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-4029-5_22 · Cited by 6
Few-labeled visual recognition for self-driving using multi-view visual-semantic representation
Wang, D., Li, J., & Zhu, S. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.02.128 · Cited by 6
Breast Cancer Detection Using Deep Learning and Machine Learning: A Comparative Analysis
Sharma, A., Kochar, B., Joshi, N., & Kumar, V. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-5113-0_39 · Cited by 5
Yang, C., Rottensteiner, F., & Heipke, C. (2020) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-v-2-2020-599-2020 · Cited by 6
TREND: Transferability-Based Robust ENsemble Design
Ravikumar, D., Kodge, S., Garg, I., & Roy, K. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3175172 · Cited by 6
ASSESSING THE SEMANTIC SIMILARITY OF IMAGES OF SILK FABRICS USING CONVOLUTIONAL NEURAL NETWORKS
Clermont, D., Dorozynski, M., Wittich, D., & Rottensteiner, F. (2020) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-v-2-2020-641-2020 · Cited by 7
Design of Festival Sentiment Classifier Based on Social Network
Yuan, H., Song, Y., Hu, J., et al. (2020) · Computational Intelligence and Neuroscience · doi:10.1155/2020/8824009 · Cited by 6
Enabling efficient ReRAM-based neural network computing via crossbar structure adaptive optimization
Liu, C., Yu, F., Qin, Z., & Chen, X. (2020) · International Symposium on Low Power Electronics and Design (ISLPED) · doi:10.1145/3370748.3406581 · Cited by 6
Defect Classification of Cross-section of Additive Manufacturing Using Image-Labeling
Lee, J.-S., Choi, B., Lee, M. G., et al. (2020) · Journal of the Korean Society of Manufacturing Process Engineers · doi:10.14775/ksmpe.2020.19.07.007 · Cited by 6
Huang, J. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09524-y · Cited by 6
Murray, J., Sargent, I. M. J., Holland, D. A., Gardiner, A., Dionysopoulou, K., Coupland, S., Hare, J. S., Zhang, C., & Atkinson, P. M. (2020) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xliii-b5-2020-185-2020 · Cited by 10
An Approach for Asbestos-related Pleural Plaque Detection
Sousa, A. M., Castelo-Fernández, C., Osaku, D., Bagatin, E., Reis, F., & Falcão, A. X. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9176605 · Cited by 6
DermaDL: Advanced Convolutional Neural Networks for Automated Melanoma Detection
Junior, J. F. R., Machado, B. B., & Amer-Yahia, S. (2020) · IEEE International Symposium on Computer-Based Medical Systems (CBMS) · doi:10.1109/cbms49503.2020.00101 · Cited by 6
Design of a Deep Post Gripping Perception Framework for Industrial Robots
Zoghlami, F., Kurrek, P., Jocas, M., Masala, G. L., & Salehi, V. (2020) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4048204 · Cited by 8
Lei, H., Teh, C., Li, H., Lee, P.-H., & Fang, W. (2020) · ASMC proceedings · doi:10.1109/asmc49169.2020.9185253 · Cited by 7
Kırbaş, İ., & Özmen, Ö. (2020) · Studies in computational intelligence · doi:10.1007/978-981-15-6321-8_4 · Cited by 6
Magistris, G. D., Üney, M., Stinco, P., et al. (2020) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion45008.2020.9190461 · Cited by 6
A Progressive Subnetwork Searching Framework for Dynamic Inference
Yang, L., He, Z., Cao, Y. K., & Fan, D. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3199703 · Cited by 7
Scene2Wav: a deep convolutional sequence-to-conditional SampleRNN for emotional scene musicalization
Sergio, G. C., & Lee, M. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09636-5 · Cited by 6
Leveraging Big Data and Machine Learning for Digital Transformation
Huang, J. (2020) · Journal of Integrated Design and Process Science · doi:10.3233/jid190020 · Cited by 6
Data Restoration by Linear Estimation of the Principal Components From Lossy Data
Lee, Y., & Choi, S. (2020) · IEEE Access · doi:10.1109/access.2020.3024809 · Cited by 6
HYPE: Predicting Blood Pressure from Photoplethysmograms in a Hypertensive Population
Sasso, A. M., Datta, S., Jeitler, M., Steckhan, N., Keßler, C. S., Michalsen, A., Bert Arnrich, A., & Böttinger, E. P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59137-3_29 · Cited by 9
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
