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 69,701–69,800 of 108,435
Recognizing cultural events in images: A study of image categorization models
Kwon, H. Y., Yun, K., Hoai, M., & Samaras, D. (2015) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301336 · Cited by 5
SAPPHIRE: an always-on context-aware computer vision system for portable devices
Venkataramani, S., Bahl, V., Hua, X., Liu, J., Li, J., Philipose, M., Priyantha, N. B., & Shoaib, M. (2015) · Design, Automation, and Test in Europe · doi:10.5555/2755753.2757157 · Cited by 5
Vocabulary Expansion Using Word Vectors for Video Semantic Indexing
Inoue, N., & Shinoda, K. (2015) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2733373.2806347 · Cited by 4
A novel target detection algorithm combining foreground and background manifold-based models
Razakarivony, S., & Jurie, F. (2015) · Machine Vision and Applications · doi:10.1007/s00138-015-0673-2 · Cited by 4
Facial expression tracking from head-mounted, partially observing cameras
Romera‐Paredes, B., Zhang, C., & Zhang, Z. (2014) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2014.6890278 · Cited by 4
Mask selective regularization for restricted Boltzmann machines
Mansanet, J., Albiol, A., Paredes, R., & Albiol, A. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.03.026 · Cited by 5
Discriminative Light Unsupervised Learning Network for Image Representation and Classification
Dong, L., He, L., & Zhang, Q. (2015) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2733373.2806325 · Cited by 4
Transfer of sparse coding representations and object classifiers across heterogeneous robots
Kira, Z. (2014) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2014.6942860 · Cited by 4
Kawano, Y., & Yanai, . (2014) · IPSJ Transactions on Computer Vision and Applications · doi:10.2197/ipsjtcva.6.83 · Cited by 5
Efficient image representation for object recognition via pivots selection
Xie, B., Liu, Y., Zhang, H., & Yu, J. Q. (2015) · Frontiers of Computer Science · doi:10.1007/s11704-015-4182-7 · Cited by 4
A Novel Fast Approach for Convolutional Networks with Small Filters Based on GPU
Jiang, W., Chen, Y., Jin, H., Luo, B., & Chi, Y. (2015) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-css-icess.2015.218 · Cited by 4
Square Localization for Efficient and Accurate Object Detection
Lu, C., Lu, Y., Chen, H., & Tang, C. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.294 · Cited by 4
Text-to-video: a semantic search engine for internet videos
Jiang, L., Yu, S.-I., Meng, D., Mitamura, T., & Hauptmann, A. G. (2015) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-015-0093-0 · Cited by 5
Recognizing cardiac magnetic resonance acquisition planes
Margeta, J., Criminisi, A., Lee, D. C., et al. (2014) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 4
Cross-Level: A Practical Strategy for Convolutional Neural Networks Based Image Classification
Liu, Y., Yin, B., Yu, J., et al. (2015) · Communications in computer and information science · doi:10.1007/978-3-662-48558-3_40 · Cited by 4
3D spatial pyramid: descriptors generation from point clouds for indoor scene classification
Romero-González, C., Martínez-Gómez, J., García-Varea, I., & Rodríguez-Ruiz, L. (2016) · Machine Vision and Applications · doi:10.1007/s00138-015-0744-4 · Cited by 4
A deep convolutional neural network for recognizing foods
Heravi, E. J., Aghdam, H. H., & Puig, D. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2228875 · Cited by 4
Li, L.-J., Shamma, D. A., Kong, X., et al. (2015) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/2801125 · Cited by 4
Rate-energy-accuracy optimization of convolutional architectures for face recognition
Bondi, L., Baroffio, L., Cesana, M., Tagliasacchi, M., Chiachia, G., & Rocha, A. D. R. (2016) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2015.12.015 · Cited by 4
A Novel Hierarchical Convolutional Neural Network for Question Answering over Paragraphs
Zheng, S., Bao, H., Zhao, J., Zhang, J., Qi, Z., & Hao, H. (2015) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat.2015.20 · Cited by 4
Viewpoint Recognition in Cardiac CT Images
Moradi, M., Codella, N., & Syeda-Mahmood, T. F. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-20309-6_21 · Cited by 4
Liao, Z., & Carneiro, G. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351666 · Cited by 4
Facial point detection using convolutional neural network transferred from a heterogeneous task
Yamashita, T., Watasue, T., Yamauchi, Y., & Fujiyoshi, H. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351298 · Cited by 4
QuickFind: Fast and contact-free object detection using a depth sensor
Zhong, H., Kanhere, S. S., & Chou, C. T. (2016) · IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops) · doi:10.1109/percomw.2016.7457114 · Cited by 4
Fast Coding of Feature Vectors Using Neighbor-to-Neighbor Search
Inoue, N., & Shinoda, K. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2481390 · Cited by 4
Deep tree-structured face: A unified representation for multi-task facial biometrics
Guo, R., Liu, L., Wang, W., Taalimi, A., Zhang, C., & Qi, H. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477585 · Cited by 4
Topic Network: Topic Model with Deep Learning for Image Classification
Pan, Z., Liu, Y., Liu, G., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-25159-2_47 · Cited by 4
Object-aware Deep Network for Commodity Image Retrieval
Fang, Z., Liu, J., Wang, Y., Li, Y., Song, H., Tang, J., & Lu, H. (2016) · International Conference on Multimedia Retrieval · doi:10.1145/2911996.2912027 · Cited by 4
Multi-staged deep learning with created coarse and appended fine categories
Hagawa, R., Ishii, Y., & Tsukizawa, S. (2015) · IAPR Asian Conference on Pattern Recognition (ACPR) · doi:10.1109/acpr.2015.7486461 · Cited by 4
Siddiqui, J. R., Andreasson, H., Driankov, D., & Lilienthal, A. J. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487800 · Cited by 4
Person re-identification based on multi-region-set ensembles
Li, W., Huang, C., Luo, B., et al. (2016) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2016.06.009 · Cited by 4
Deep Discriminative and Generative Models for Pattern Recognition
Deng, L., & Jaitly, N. (2015) · Cited by 4
The Future of Automated Mobile Eye Diagnosis
Ludwig, C. A., Shan, M. X., Nguyen, N. P., Choi, D. H., Ku, V., & Lam, C. (2016) · Journal of Mobile Technology in Medicine · doi:10.7309/jmtm.5.2.7 · Cited by 3
Pattern recognition between science and engineering: A red herring?
Pelillo, M., Scantamburlo, T., & Schiaffonati, V. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.06.030 · Cited by 4
He, Y., Yu, L.-C., Yang, C., Lai, K.-H. R., & Liu, W. (2016) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s16-1039 · Cited by 4
A null model for cortical representations with grandmothers galore
Kreiman, G. (2016) · Language Cognition and Neuroscience · doi:10.1080/23273798.2016.1218033 · Cited by 4
Sarvadevabhatla, R. K., Surya, S., Kruthiventi, S. S. S., et al. (2016) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2964284.2967208 · Cited by 4
Efficient Training of Small Kernel Convolutional Neural Networks using Fast Fourier Transform
Highlander, T. C. (2015) · Journal of Bioresource Management · Cited by 4
Classifying weight training workouts with deep convolutional neural networks
Park, J. (2016) · International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct (MobileHCI Adjunct) · doi:10.1145/2957265.2961861 · Cited by 4
Online video tracking using collaborative convolutional networks
Guan, H., Xue, X., & An, Z. (2016) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2016.7552897 · Cited by 4
General-Purpose Big Data Processing Systems
Sakr, S. (2016) · SpringerBriefs in computer science · doi:10.1007/978-3-319-38776-5_2 · Cited by 6
A Holistic Approach for Data-Driven Object Cutout
Xu, H., Li, Y., Chen, W., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54181-5_16 · Cited by 4
Weakly supervised object detection with correlation and part suppression
Wan, F., Wei, P., Han, Z., Fu, K., & Ye, Q. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7533038 · Cited by 3
A Convolutional Network Model of the Primate Middle Temporal Area
Tripp, B. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-44781-0_12 · Cited by 4
Weak supervision for detecting object classes from activities
Srikantha, A., & Gall, J. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.09.006 · Cited by 3
Kameda, T., Kimura, M., & Nakashima, Y. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46672-9_39 · Cited by 4
Semi-supervised Learning for Convolutional Neural Networks Using Mild Supervisory Signals
Shinozaki, T. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46681-1_46 · Cited by 4
Analytics-Driven Visualization on Digital Directory via Screen-Smart Device Interactions
Cheung, M., She, J. P. M., & Park, S. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2614222 · Cited by 4
Deep Q-Learning for Home Automation
Gokul, V., Kannan, P., Kumar, S., et al. (2016) · International Journal of Computer Applications · doi:10.5120/ijca2016911873 · Cited by 4
Deep Twin Support Vector Machine
Li, D., Tian, Y., & Honggui, X. (2014) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2014.18 · Cited by 4
Determining Player Skill in the Game of Go with Deep Neural Networks
Moudřík, J., & Neruda, R. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49001-4_15 · Cited by 4
Backbone Dihedral Angle Prediction
Zimmermann, O. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-6406-2_7 · Cited by 4
A Novel Deep Learning Network Architecture with Cross-Layer Neurons
Zeng, Y., Yu, N., Pan, Y. F., et al. (2016) · doi:10.1109/bdcloud-socialcom-sustaincom.2016.27 · Cited by 4
Learning-based leaf occlusion detection in surveillance video
Liu, J., Chen, L., Tian, J., et al. (2016) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea.2016.7603728 · Cited by 4
Bi-Level Multi-column Convolutional Neural Networks for Facial Landmark Point Detection
Xu, Y., & Gao, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48881-3_37 · Cited by 4
Convolutional fitted Q iteration for vision-based control problems
Zhao, D., Zhu, Y., Lv, L., et al. (2016) · doi:10.1109/ijcnn.2016.7727794 · Cited by 4
Hagiwara, Y., Inoue, M., & Tadahiro, T. (2016) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2016.10.527 · Cited by 4
A Deep Learning Approach to Mid-air Gesture Interaction for Mobile Devices from Time-of-Flight Data
Kopinski, T., Sachara, F., & Handmann, U. (2016) · International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous) · doi:10.1145/2994374.2994392 · Cited by 4
Pollen Recognition for Allergy and Asthma Management Using GIST Features
Khanzhina, N., & Lane, E. (2016) · Communications in computer and information science · doi:10.1007/978-3-319-49700-6_51 · Cited by 4
A Simplified Phase Model for Simulation of Oscillator-Based Computing Systems
Fang, Y., Yashin, V. V., Jennings, B. B., et al. (2016) · ACM Journal on Emerging Technologies in Computing Systems · doi:10.1145/2976743 · Cited by 4
Complex convolution Kernel for deep networks
Li, K., Shi, H., & Hu, Q. (2016) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp.2016.7752631 · Cited by 4
Semantic-Based Brain MRI Image Segmentation Using Convolutional Neural Network
Chou, Y., Lee, D. J., & Zhang, D. L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-50835-1_56 · Cited by 4
Surveillance Based Crowd Counting via Convolutional Neural Networks
Zhang, D., Li, Z., & Liu, P. (2016) · Communications in computer and information science · doi:10.1007/978-981-10-3476-3_17 · Cited by 4
Cao, L., Liu, Z., He, X., Cao, Y., & Li, K. (2016) · IEEE International Conference on Smart Cloud (SmartCloud) · doi:10.1109/smartcloud.2016.38 · Cited by 3
Ai, S., Jia, C., & Chen, Z. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-51811-4_15 · Cited by 4
Low-Quality Video Face Recognition with Deep Networks and Polygonal Chain Distance
Herrmann, C., Willersinn, D., & Beyerer, J. (2016) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2016.7797061 · Cited by 4
Localize heavily occluded human faces via deep segmentation
Zhang, K., Huang, Y., He, R., et al. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532771 · Cited by 4
Reducing adaptation latency for multi-concept visual perception in outdoor environments
Wigness, M., Rogers, J. G., Navarro‐Serment, L. E., et al. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759432 · Cited by 4
From coarse- to fine-grained implementation of edge-directed interpolation using a GPU
Wu, J., Li, W., & Jeon, G. (2017) · Information Sciences · doi:10.1016/j.ins.2017.01.002 · Cited by 4
Thinning deep neural networks for sketch recognition
Ahn, P., Shin, D. H., & Kim, J. (2016) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2016.7820769 · Cited by 4
Audience Behavior Mining by Integrating TV Ratings with Multimedia Contents
Hinami, R., & Satoh, S. (2016) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2016.0018 · Cited by 4
Learning based hole filling method using deep convolutional neural network for view synthesis
Lim, H.-T., Kim, H. G., & Ro, Y. M. (2016) · Electronic Imaging · doi:10.2352/issn.2470-1173.2016.14.ipmva-376 · Cited by 4
Srinivasa, K. G., Sowmya, B. J., Kumar, D., et al. (2016) · International Journal of Applied Evolutionary Computation · doi:10.4018/ijaec.2016100105 · Cited by 4
Semi-Supervised Learning with Bidirectional Adaptive Pairwise Encoding
Yuan, J., & Yu, J. (2016) · IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla.2016.0119 · Cited by 4
Fisher kernels match deep models
Azim, T. (2017) · Electronics Letters · doi:10.1049/el.2016.3320 · Cited by 4
Automatic segmentation of the left atrium from MR images via semantic information
Deng, C., & Zhang, X. (2016) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2016.7844745 · Cited by 5
Recognition of individual object in focus people group based on deep learning
Liu, H., Wu, F. Q., Chen, Q., & Yong, P. (2016) · International Conference on Audio, Language and Image Processing · doi:10.1109/icalip.2016.7846607 · Cited by 4
On the use of convolutional neural networks for graphical model-based human pose estimation
Vu, H. T., Cheng, E., Wilkinson, R. H., & Lech, M. (2017) · International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom) · doi:10.1109/sigtelcom.2017.7849801 · Cited by 4
Traffic sign recognition using visual attribute learning and convolutional neural network
Qian, R., Yue, Y., Coenen, F., & Zhang, B. (2016) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2016.7860932 · Cited by 4
Detecting and inferring repetitive elements with accurate locations and shapes from façades
Lian, Y., Shen, X., & Hu, Y. (2017) · The Visual Computer · doi:10.1007/s00371-017-1355-z · Cited by 4
The 3D-Pitoti Dataset: A Dataset for high-resolution 3D Surface Segmentation
Poier, G., Seidl, M., Zeppelzauer, M., Reinbacher, C., Schaich, M., Bellandi, G., Marretta, A., & Bischof, H. (2017) · TUGraz OPEN Library (Graz University of Technology) · Cited by 4
Object-Aware Dictionary Learning with Deep Features
Xie, Y., Porikli, F., & He, X. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54184-6_15 · Cited by 4
PicMarker: Data-Driven Image Categorization Based on Iterative Clustering
Hu, J., Sun, Z., Li, B. H., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54190-7_11 · Cited by 4
Stacked Overcomplete Independent Component Analysis for Action Recognition
Liu, Z., Tian, Y., & Wang, Z. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54184-6_23 · Cited by 4
Deep Second-Order Siamese Network for Pedestrian Re-identification
Deng, X., Ma, B., Chang, H., Shan, S., & Chen, X. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54184-6_20 · Cited by 4
Truncating Wide Networks Using Binary Tree Architectures
Zhang, Y., Özay, M., Li, S., & Okatani, T. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.231 · Cited by 4
Glance and Glimpse Network: A Stochastic Attention Model Driven by Class Saliency
Li, M., Ge, S. S., & Lee, T. H. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54526-4_42 · Cited by 4
Experimental Demonstration of Firing Rate Neural Networks Based on Metal-Oxide Memristive Crossbars
Bayat, F. M., Prezioso, M., & Chakrabarti, B. (2017) · Springer eBooks · doi:10.1007/978-3-319-54313-0_6 · Cited by 4
Multiple Objects Fusion Tracker Using a Matching Network for Adaptively Represented Instance Pairs
Oh, S.-I., & Kang, H.-B. (2017) · Sensors · doi:10.3390/s17040883 · Cited by 4
Sparse-coded cross-domain adaptation from the visual to the brain domain
Ghaemmaghami, P., Nabi, M., Yan, Y., & Sebe, N. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900295 · Cited by 4
Fast feedforward non-parametric deep learning network with automatic feature extraction
Angelov, P. P., Gu, X., & Príncipe, J. C. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7965899 · Cited by 4
Learning with proxy supervision for end-to-end visual learning
Čermák, J., & Angelova, A. (2017) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2017.7995690 · Cited by 4
Relationship between Complexity and Precision of Convolutional Neural Networks
Ke, X., Cao, W., & Lv, F. (2017) · International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE) · doi:10.2991/isaeece-17.2017.62 · Cited by 4
Kim, T.-H., & Kim, S. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2262270 · Cited by 4
Using Deep Learning for Image Similarity in Product Matching
Rivas-Sánchez, M., Guerrero-Lebrero, M. P., Guerrero, E., Bárcena‐González, G., Martel, J., & Galindo, P. L. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59153-7_25 · Cited by 4
Klinger, R. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59569-6_39 · Cited by 5
The evolution of neural network-based chart patterns
Ha, M. H., & Moon, B.-R. (2017) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3071178.3071192 · Cited by 4
Comparison and Analysis of the Open-Source Frameworks for Deep Learning
Dong-sheng, G. A. O., Zhao, Y., Gao, J., et al. (2017) · DEStech Transactions on Computer Science and Engineering · doi:10.12783/dtcse/mcsse2016/10975 · Cited by 4
Zhang, Q., Li, Z., Hu, X., & Zhang, J. (2016) · IEEE International Conference on Progress in Informatics and Computing · doi:10.1109/pic.2016.7949501 · Cited by 4
Odaklanan Nöron focusing neuron
Çam, İ., & Faik Boray, T. E. K. (2017) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2017.7960632 · Cited by 4
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
