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 38,301–38,400 of 108,435
Aerial Detection in Maritime Scenarios Using Convolutional Neural Networks
Cruz, G. C. S., & Bernardino, A. J. M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48680-2_33 · Cited by 23
3D Face Alignment in the Wild: A Landmark-Free, Nose-Based Approach
Zavan, F. H. D. B., Nascimento, A. C. P., Silva, L. P. E., Bellon, O. R. P., & da Silva, L. T. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48881-3_40 · Cited by 17
Where and What to Eat: Simultaneous Restaurant and Dish Recognition from Food Image
Wang, H., Min, W., Li, X., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48890-5_51 · Cited by 19
Fast, Deep Detection and Tracking of Birds and Nests
Wang, Q., Rasmussen, C. E., & Song, C. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-50835-1_14 · Cited by 19
Smart Content Recognition from Images Using a Mixture of Convolutional Neural Networks
Connie, T., Al-Shabi, M., & Goh, M. K. O. (2017) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-6451-7_2 · Cited by 19
Chitrakar, P., Zhang, C., Warner, G., & Liao, X. (2016) · 2016 IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2016.0110 · Cited by 20
Overview: Video recognition from handcrafted method to deep learning method
Xiao, X., Xu, D., & Wan, W. (2016) · International Conference on Audio, Language and Image Processing · doi:10.1109/icalip.2016.7846652 · Cited by 19
Gómez, S., Santos, J., Rodríguez, F. J. I., & Juez, F. J. D. C. (2017) · Entropy · doi:10.3390/e19030103 · Cited by 19
Nadine: A Social Robot that Can Localize Objects and Grasp Them in a Human Way
Magnenat‐Thalmann, N., Tian, L., & Yao, F. (2017) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-4235-5_1 · Cited by 19
Zhang, J., Ma, S., Sameki, M., et al. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1011-0 · Cited by 19
Expanded autoencoder recommendation framework and its application in movie recommendation
Yi, B., Shen, X., Zhang, Z., et al. (2016) · International Conference on Software, Knowledge, Information Management & Applications (SKIMA) · doi:10.1109/skima.2016.7916236 · Cited by 19
Frame-Transformer Emotion Classification Network
Gao, J., Fu, Y., Jiang, Y., & Xue, X. (2017) · International Conference on Multimedia Retrieval · doi:10.1145/3078971.3079030 · Cited by 18
Deconvolutional Neural Network for Pupil Detection in Real-World Environments
Vera-Olmos, F. J., & Malpica, N. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59773-7_23 · Cited by 19
Bi-linearly weighted fractional max pooling
Hang, S. T., & Aono, M. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4840-5 · Cited by 19
Video-Based Fire Detection with Saliency Detection and Convolutional Neural Networks
Shi, L., Long, F., Lin, C.-H., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59081-3_36 · Cited by 19
Large-scale image retrieval using transductive support vector machines
Çevıkalp, H., Elmas, M., & Özkan, S. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.07.004 · Cited by 19
Chen, B.-C., Ghosh, P., Morariu, V. I., et al. (2017) · IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.234 · Cited by 19
Improved Object Detection With Iterative Localization Refinement in Convolutional Neural Networks
Cheng, K., Chen, Y., & Fang, W. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2730258 · Cited by 19
Convolutional neural network application to plant detection, based on synthetic imagery
Pearlstein, L., Kim, M., & Seto, W. (2016) · IEEE Applied Imagery Pattern Recognition Workshop · doi:10.1109/aipr.2016.8010596 · Cited by 19
Garg, P., Davenport, E. M., Murugesan, G. K., Wagner, B. C., Whitlow, C. T., Maldjian, J. A., & Montillo, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66179-7_43 · Cited by 21
Fish Species Recognition Based on CNN Using Annotated Image
Miyazono, T., & Saitoh, T. (2017) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-6451-7_19 · Cited by 19
Accelerating Matrix Multiplication in Deep Learning by Using Low-Rank Approximation
Osawa, K., Sekiya, A., Naganuma, H., & Yokota, R. (2017) · International Conference on High Performance Computing & Simulation (HPCS) · doi:10.1109/hpcs.2017.37 · Cited by 21
Combining CNNs and Pattern Matching for Question Interpretation in a Virtual Patient Dialogue System
Jin, L., White, M., Jaffe, E., Zimmerman, L., & Danforth, D. R. (2017) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/w17-5002 · Cited by 19
A Deep Residual convolutional neural network for facial keypoint detection with missing labels
Wu, S., Xu, J., Zhu, S., & Guo, H. (2017) · Signal Processing · doi:10.1016/j.sigpro.2017.11.003 · Cited by 19
Nie, G.-Y., Liu, Y., & Wang, Y. (2017) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar-adjunct.2017.35 · Cited by 19
Automatic Music Video Generation Based on Simultaneous Soundtrack Recommendation and Video Editing
Lin, J., Wei, W.-L., Yang, J. C.-Y., et al. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3123399 · Cited by 19
A facial expression recognition method based on ensemble of 3D convolutional neural networks
Sun, W., Zhao, H., & Jin, Z. (2017) · Neural Computing and Applications · doi:10.1007/s00521-017-3230-2 · Cited by 19
A Study on Deep Belief Net for Branch Prediction
Mao, Y., Shen, J., & Gui, X. (2017) · IEEE Access · doi:10.1109/access.2017.2772334 · Cited by 19
Feature weighting for multinomial kernel logistic regression and application to action recognition
Ouyed, O., & Allili, M. S. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.10.024 · Cited by 19
Stefanov, K., Beskow, J., & Salvi, G. (2019) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2019.2927941 · Cited by 18
Comparison between CNN and Haar classifiers for surgical instrumentation classification
Useche-Murillo, P. C., Moreno, R. J., & Pinzon-Arenas, J. O. (2017) · Contemporary Engineering Sciences · doi:10.12988/ces.2017.711157 · Cited by 19
Hand pose estimation with multi-scale network
Hu, Z., Hu, Y., Wu, B., Liu, J., Han, D., & Kurfess, T. R. (2017) · Applied Intelligence · doi:10.1007/s10489-017-1092-z · Cited by 19
Very Fast Semantic Image Segmentation Using Hierarchical Dilation and Feature Refining
Ning, Q., Zhu, J., & Chen, C. (2017) · Cognitive Computation · doi:10.1007/s12559-017-9530-0 · Cited by 19
Enhancing Function Approximation Abilities of Neural Networks by Training Derivatives
Avrutskiy, V. I. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2979706 · Cited by 19
Head and Body Orientation Estimation Using Convolutional Random Projection Forests
Lee, D., Yang, M., & Oh, S. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2784424 · Cited by 20
Automatic Document Metadata Extraction Based on Deep Networks
Liu, R., Gao, L., An, D. U., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-73618-1_26 · Cited by 19
Knitted fabric structure recognition based on deep learning
Xiao, Z., Liu, X., Wu, J., Geng, L., Sun, Y., Zhang, F., & Tong, J. (2018) · Journal of the Textile Institute · doi:10.1080/00405000.2017.1422309 · Cited by 18
Unsupervised learning from videos using temporal coherency deep networks
Redondo-Cabrera, C., & López-Sastre, R. J. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.08.003 · Cited by 18
FPGA-based training of convolutional neural networks with a reduced precision floating-point library
DiCecco, R., Sun, L., & Chow, P. (2017) · International Conference on Field-Programmable Technology (FPT) · doi:10.1109/fpt.2017.8280150 · Cited by 20
Deep expectation for estimation of fingerprint orientation fields
Schuch, P., Schulz, S.-D., & Busch, C. (2017) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/btas.2017.8272697 · Cited by 19
Evaluation of Texture Descriptors for Validation of Counterfeit Documents
Berenguel, A., Terrades, O. R., Lladós, J., et al. (2017) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2017.204 · Cited by 19
An interactive facial-expression training platform for individuals with autism spectrum disorder
Tsangouri, C., Li, W., Zhu, Z., Abtahi, F., & Ro, T. (2016) · IEEE MIT Undergraduate Research Technology Conference (URTC) · doi:10.1109/urtc.2016.8284067 · Cited by 19
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks
Racah, E., Ko, S., Sadowski, P., Bhimji, W., Tull, C., Oh, S.-Y., Baldi, P., & Prabhat (2016) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2016.0160 · Cited by 33
Problem solving chatbot for data structures
Shah, A., Jain, B., Agrawal, B., Jain, S., & Shim, S. S. Y. (2018) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc.2018.8301734 · Cited by 21
Predicting Occupancy Distributions of Walking Humans With Convolutional Neural Networks
Doellinger, J., Spies, M., & Burgard, W. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2800780 · Cited by 20
Convolutional neural networks for computer vision-based detection and recognition of dumpsters
Ramírez, I., Cuesta‐Infante, A., Pantrigo, J. J., Montemayor, A. S., Moreno, J. L., Alonso, V., Anguita, G., & Palombarani, L. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3390-8 · Cited by 21
Object Captioning and Retrieval with Natural Language
Nguyen, A., Tran, Q. D., Do, T.-T., Reid, I. D., Caldwell, D. G., & Tsagarakis, N. G. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00316 · Cited by 20
Attend It Again: Recurrent Attention Convolutional Neural Network for Action Recognition
Yang, H., Zhang, J., Li, S., Lei, J. W., & Chen, S. (2018) · Applied Sciences · doi:10.3390/app8030383 · Cited by 20
3D Interpreter Networks for Viewer-Centered Wireframe Modeling
Wu, J., Xue, T., Lim, J. J., Tian, Y., Tenenbaum, J. B., Torralba, A. B., & Freeman, W. T. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1074-6 · Cited by 22
Action recognition with motion map 3D network
Sun, Y., Wu, X., Yu, W., & Yu, F. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.02.028 · Cited by 19
Particle identification in camera image sensors using computer vision
Winter, M., Bourbeau, J., Bravo, S., Campos, F., Meehan, M., Peacock, J., Ruggles, T., Schneider, C., Simons, A. L., & Vandenbroucke, J. A. (2018) · Astroparticle Physics · doi:10.1016/j.astropartphys.2018.08.009 · Cited by 21
VRID-1: A basic vehicle re-identification dataset for similar vehicles
Li, X., Yuan, M., Jiang, Q., et al. (2017) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2017.8317817 · Cited by 19
Image Sentiment Analysis Using Convolutional Neural Network
Kumar, A., & Jaiswal, A. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-76348-4_45 · Cited by 20
Robot Foraging: Autonomous Sample Return in a Large Outdoor Environment
Gu, Y., Strader, J., Ohi, N., Harper, S., Lassak, K., Yang, C., Kogan, L., Hu, B.-Y., Gramlich, M., Kavi, R., et al. (2018) · IEEE robotics & automation magazine · doi:10.1109/mra.2018.2803174 · Cited by 17
van der Sommen, F., Klomp, S. R., Swager, A.-F., Zinger, S., Curvers, W. L., Bergman, J. J. G. H. M., Schoon, E. J., & de With, P. H. N. (2018) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2018.02.007 · Cited by 19
Park, S., & Thorpe, M. (2018) · 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/cvpr.2018.00820 · Cited by 19
Neural network control of networked redundant manipulator system with weight initialization method
Jiang, N., Xu, J., & Shu, Z. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.04.039 · Cited by 19
Multi-View Ground-Based Cloud Recognition by Transferring Deep Visual Information
Zhang, Z., Li, D., Liu, S., Xiao, B., & Cao, X. (2018) · Applied Sciences · doi:10.3390/app8050748 · Cited by 19
Investigation in Spatial-Temporal Domain for Face Spoof Detection
Sun, Z., Li, S., & Li, Q. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461942 · Cited by 19
Jiao, J., Deng, Z., Arain, Q. A., et al. (2018) · Wireless Personal Communications · doi:10.1007/s11277-018-5725-2 · Cited by 19
A deep learning pipeline for Indian dance style classification
Dewan, S., Agarwal, S., & Singh, N. (2018) · International Conference on Machine Vision · doi:10.1117/12.2309445 · Cited by 20
Look-Up Table Unit Activation Function for Deep Convolutional Neural Networks
Wang, M., Liu, B., & Foroosh, H. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00139 · Cited by 19
Small infrared target detection by data-driven proposal and deep learning-based classification
Kim, S., & Ryu, J. (2018) · doi:10.1117/12.2304677 · Cited by 19
From Feature To Paradigm: Deep Learning In Machine Translation
Costa‐jussà, M. R. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11198 · Cited by 18
AMI-Net: Convolution Neural Networks With Affine Moment Invariants
Hao, Y., Li, Q., Mo, H., Zhang, H., & Li, H. (2018) · IEEE Signal Processing Letters · doi:10.1109/lsp.2018.2843296 · Cited by 19
Jafari, A., Hosseini, M., Kulkarni, A., et al. (2018) · ACM Great Lakes Symposium on VLSI (GLSVLSI) · doi:10.1145/3194554.3194634 · Cited by 19
Rahmani, K., & Mayer, H. (2018) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iv-2-223-2018 · Cited by 19
Melanoma Segmentation and Classification in Clinical Images Using Deep Learning
Ge, Y., Li, B., Zhao, Y., Guan, E., & Yan, W. (2018) · International Conference on Machine Learning and Computing (ICMLC) · doi:10.1145/3195106.3195164 · Cited by 21
Evaluating Brush Movements for Chinese Calligraphy: A Computer Vision Based Approach
Xu, P., Wang, L., Guan, Z., et al. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/146 · Cited by 19
Human Pose and Path Estimation from Aerial Video Using Dynamic Classifier Selection
Perera, A. G., Law, Y. W., & Chahl, J. (2018) · Cognitive Computation · doi:10.1007/s12559-018-9577-6 · Cited by 22
Feature Selection for Unsupervised Domain Adaptation Using Optimal Transport
Gautheron, L., Redko, I., & Lartizien, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-10928-8_45 · Cited by 22
Learning safe recovery trajectories with deep neural networks for unmanned aerial vehicles
Khan, A., & Hebert, M. (2018) · Proceedings - IEEE Aerospace Conference · doi:10.1109/aero.2018.8396807 · Cited by 19
A case study on transfer learning in convolutional neural networks
Gürkaynak, C. D., & Arıca, N. (2018) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2018.8404642 · Cited by 19
Conformal Mapping of a 3D Face Representation onto a 2D Image for CNN Based Face Recognition
Kittler, J., Koppen, P., Kopp, P., et al. (2018) · International Conference on Biometrics · doi:10.1109/icb2018.2018.00029 · Cited by 19
Genç, A., & Ekenel, H. K. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6409-3 · Cited by 19
Chae, M.-J., Park, K., Bang, J., Suh, S., Park, J.-H., Kim, N. J., & Park, L. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462678 · Cited by 18
Li, R., Shu, Y., Su, J., Feng, H.-C., Zhang, G., Wang, J., & Liu, H. T. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty2708 · Cited by 22
3D convolutional neural network for feature extraction and classification of fMRI volumes
Vu, H., Kim, H., & Lee, J. (2018) · International Workshop on Pattern Recognition in NeuroImaging · doi:10.1109/prni.2018.8423964 · Cited by 20
Automatic Convolutional Neural Network Selection for Image Classification Using Genetic Algorithms
Tian, H., Pouyanfar, S., Chen, J., Chen, S., & Iyengar, S. S. (2018) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2018.00071 · Cited by 21
Deep Visual Discomfort Predictor for Stereoscopic 3D Images
Oh, H., Ahn, S., Lee, S., & Bovik, A. C. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2851670 · Cited by 20
Image Classification for Robotic Plastering with Convolutional Neural Network
Bard, J., Bidgoli, A., & Wei, W. (2018) · Springer eBooks · doi:10.1007/978-3-319-92294-2_1 · Cited by 19
Aggarwal, C. C. (2018) · Springer eBooks · doi:10.1007/978-3-319-94463-0_8 · Cited by 28
Review of Deep Learning Techniques for Gender Classification in Images
Dwivedi, N., & Singh, D. K. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-0761-4_102 · Cited by 20
Walking Through the Deep: Gait Analysis for User Authentication Through Deep Learning
Giorgi, G., Martinelli, F., Saracino, A., & Sheikhalishahi, M. (2018) · IFIP advances in information and communication technology · doi:10.1007/978-3-319-99828-2_5 · Cited by 22
Pyramid-Based Fully Convolutional Networks for Cell Segmentation
Zhao, T., & Yin, Z. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00937-3_77 · Cited by 20
Power Market Price Forecasting via Deep Learning
Zhu, Y., Dai, R., Liu, G., Wang, Z., & Lu, S. (2018) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2018.8591581 · Cited by 26
CNN-based Prediction for Lossless Coding of Photographic Images
Schiopu, I., Liu, Y., & Munteanu, A. (2018) · Picture Coding Symposium · doi:10.1109/pcs.2018.8456311 · Cited by 19
Deep Power Forecasting Model for Building Attached Photovoltaic System
Du, L., Zhang, L., & Tian, X. (2018) · IEEE Access · doi:10.1109/access.2018.2869424 · Cited by 21
Learning Local and Deep Features for Efficient Cell Image Classification Using Random Forests
Oraibi, Z. A., Yousif, H. A., Hafiane, A., et al. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451287 · Cited by 19
SCOTI: Science Captioning of Terrain Images for data prioritization and local image search
Qiu, D., Rothrock, B., Islam, T., Didier, A., Sun, V. Z., Mattmann, C. A., & Ono, M. (2020) · Planetary and Space Science · doi:10.1016/j.pss.2020.104943 · Cited by 19
Benchmarking parts based face processing in-the-wild for gender recognition and head pose estimation
Zavan, F. H. D. B., Bellon, O. R. P., da Silva, L. T., & Medioni, G. G. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.09.023 · Cited by 20
Kwak, M., Park, J., Woo, J., & Hwang, H. (2018) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2018.2862139 · Cited by 19
NOISY IMAGE CLASSIFICATION USING HYBRID DEEP LEARNING METHODS
Roy, S. S., Ahmed, M., & Akhand, M. A. H. (2018) · Journal of Information and Communication Technology · doi:10.32890/jict2018.17.2.8253 · Cited by 31
Hierarchy of Alternating Specialists for Scene Recognition
Kim, H. J., & Frahm, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01252-6_28 · Cited by 17
Shallow Convolutional Neural Networks for Pattern Recognition Problems
Gorokhovatskyi, O., & Peredrii, O. (2018) · IEEE International Conference on Data Stream Mining & Processing (DSMP) · doi:10.1109/dsmp.2018.8478540 · Cited by 20
CoNNA – Compressed CNN Hardware Accelerator
STRUHARIK, R. J. R., Vukobratović, B. Z., Erdeljan, A., & Rakanovic, D. (2018) · Euromicro Conference on Digital System Design (DSD) · doi:10.1109/dsd.2018.00070 · Cited by 19
Training Binary Weight Networks via Semi-Binary Decomposition
Hu, Q., Li, G., Wang, P., Zhang, Y., & Cheng, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01261-8_39 · Cited by 18
Deep Learning Based Approach for Gender Classification
Haseena, S., Bharathi, S., Padmapriya, I., et al. (2018) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca.2018.8474919 · Cited by 19
Traffic-Sign Spotting in the Wild via Deep Features
Guo, J., Lu, J., Qu, Y., et al. (2018) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2018.8500650 · Cited by 18
Online Filter Weakening and Pruning for Efficient Convnets
Zhou, Z., Zhou, W., Hong, R., & Li, H. (2018) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2018.8486540 · Cited by 19
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
