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 35,201–35,300 of 108,435
Localization of the slab information in factory scenes using deep convolutional neural networks
Lee, S. J., & Kim, S. W. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.01.026 · Cited by 22
Distributed embedded deep learning based real-time video processing
Zhang, W., Zhao, D., Xu, L., Li, Z., Gong, W., & Zhou, J. (2016) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2016.7844524 · Cited by 22
Deep learning in assessment of drill condition on the basis of images of drilled holes
Kurek, J., Świderski, B., Jegorowa, A., et al. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2266254 · Cited by 22
Variant SemiBoost for Improving Human Detection in Application Scenes
Wu, S., Wong, H., & Wang, S. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2672686 · Cited by 23
Towards vegetation species discrimination by using data-driven descriptors
Nogueira, K., Santos, J. A. D., Fornazari, T., Silva, T. S. F., Morellato, L. P. C., & Torres, R. D. S. (2016) · IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) · doi:10.1109/prrs.2016.7867024 · Cited by 21
Efficient Model Averaging for Deep Neural Networks
Opitz, M., Possegger, H., & Bischof, H. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54184-6_13 · Cited by 22
Spatial pyramid deep hashing for large-scale image retrieval
Zhao, W., Luo, H., Peng, J., & Jianping Fan, B. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.03.021 · Cited by 23
DropRegion training of inception font network for high-performance Chinese font recognition
Huang, S., Zhong, Z., Jin, L., Zhang, S., & Wang, H. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.018 · Cited by 21
Age classification with deep learning face representation
Huang, J., Li, B., Zhu, J., & Chen, J. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4646-5 · Cited by 22
Context-aware mathematical expression recognition: An end-to-end framework and a benchmark
He, W., Luo, Y., Yin, F., et al. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900135 · Cited by 22
Li, F., Li, S., Zhu, C., Lan, X., & Chang, H. (2017) · Remote Sensing · doi:10.3390/rs9050494 · Cited by 22
Extraction and Classification of Diving Clips from Continuous Video Footage
Nibali, A., He, Z., Morgan, S., et al. (2017) · IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.18 · Cited by 22
Chen, T., & Lu, S. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2704107 · Cited by 23
A fully trainable network with RNN-based pooling
Li, S., Li, W., Cook, C., Zhu, C., & Gao, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.02.004 · Cited by 24
Automatic detection of motion artifacts in MR images using CNNS
Meding, K., Loktyushin, A., & Hirsch, M. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952268 · Cited by 21
Multimodal Trip Hazard Affordance Detection on Construction Sites
McMahon, S., Sünderhauf, N., Upcroft, B., & Milford, M. J. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2719763 · Cited by 26
3D Hand-Object Pose Estimation from Depth with Convolutional Neural Networks
Goudie, D., & Galata, A. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.58 · Cited by 22
Monteiro, J., Aires, J. P., Granada, R., Barros, R. C., & Meneguzzi, F. R. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966130 · Cited by 22
SparseConnect: regularising CNNs on fully connected layers
Xu, Q., & Pan, G. (2017) · Electronics Letters · doi:10.1049/el.2017.2621 · Cited by 22
Jiang, Y., Wu, H., & Dong, J. (2017) · International Conference on Medical and Health Informatics (ICMHI) · doi:10.1145/3107514.3107523 · Cited by 22
Adaptation in the visual cortex: a case for probing neuronal populations with natural stimuli
Snow, M., Coen-Cagli, R., & Schwartz, O. (2017) · F1000Research · doi:10.12688/f1000research.11154.1 · Cited by 22
Multiview Canonical Correlation Analysis Networks for Remote Sensing Image Recognition
Yang, X., Liu, W., Tao, D., et al. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2738671 · Cited by 22
Zheng, J., Miao, S., & Liao, R. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66185-8_73 · Cited by 21
Hashing with Residual Networks for Image Retrieval
Conjeti, S., Roy, A. G., Katouzian, A., & Navab, N. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66179-7_62 · Cited by 29
Shahriari, B., Moh, M., & Moh, T.-S. (2017) · International Conference on High Performance Computing & Simulation (HPCS) · doi:10.1109/hpcs.2017.36 · Cited by 22
Comparison of objective functions in CNN-based prostate magnetic resonance image segmentation
Mun, J., Jang, W.-D., Sung, D. J., & Kim, C. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8297005 · Cited by 25
Multi-view local discrimination and canonical correlation analysis for image classification
Han, L., Jing, X., & Wu, F. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.09.045 · Cited by 22
A deep cascade of neural networks for image inpainting, deblurring and denoising
Zhao, G., Liu, J., Jiang, J., et al. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5320-7 · Cited by 22
Loroch, D. M., Pfreundt, F., Wehn, N., & Keuper, J. (2017) · doi:10.1145/3146347.3146348 · Cited by 23
Fonnegra, R. D., Blair, B., & Díaz, G. M. (2017) · IEEE Colombian Conference on Communications and Computing (COLCOM) · doi:10.1109/colcomcon.2017.8088219 · Cited by 24
Accurate Sentence Matching with Hybrid Siamese Networks
Nicosia, M., & Moschitti, A. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3133156 · Cited by 24
Automatic detection of fruits in coffee crops from aerial images
Carrijo, G. L. A., Oliveira, D. E., de Assis, G. A., et al. (2017) · Latin American Robotics Symposium and Brazilian Symposium on Robotics (LARS-SBR) · doi:10.1109/sbr-lars-r.2017.8215283 · Cited by 22
A Case of On-Chip Memory Subsystem Design for Low-Power CNN Accelerators
Wang, Y., Li, H., & Li, X. (2017) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2017.2778060 · Cited by 22
Computer-aided diagnosis of four common cutaneous diseases using deep learning algorithm
Zhang, X., Wang, S., Liu, J., & Tao, C. (2017) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2017.8217850 · Cited by 23
Aksara jawa text detection in scene images using convolutional neural network
Afakh, M. L., Risnumawan, A., Anggraeni, M. E., Tamara, M. N., & Ningrum, E. S. (2017) · International Electronics Symposium (IES) · doi:10.1109/kcic.2017.8228567 · Cited by 24
Comparison of shallow and deep learning models for classification of Lasem batik patterns
Handhayani, T., Hendryli, J., & Hiryanto, L. (2017) · International Conference on Informatics and Computational Sciences (ICICoS) · doi:10.1109/icicos.2017.8276330 · Cited by 22
Zhang, R., Wang, Q., & Lu, Y. (2017) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2017.324 · Cited by 23
Sketch-Based Manga Retrieval Using Deep Features
Narita, R., Tsubota, K., Yamasaki, T., & Aizawa, K. (2017) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2017.291 · Cited by 22
An autonomous elevator button recognition system based on convolutional neural networks
Dong, Z., Zhu, D., & Meng, M. Q. ‐. (2017) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2017.8324801 · Cited by 21
Optimizing Convolutional Neural Networks on the Sunway TaihuLight Supercomputer
Zhao, W., Fu, H., Fang, J., Zheng, W., Gan, L., & Yang, G. (2018) · ACM Transactions on Architecture and Code Optimization · doi:10.1145/3177885 · Cited by 22
Adaptive RGB Image Recognition by Visual-Depth Embedding
Cai, Z., Long, Y., & Shao, L. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2806839 · Cited by 22
Font generation based on least squares conditional generative adversarial nets
Lin, X., Li, J., Zeng, H., et al. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5457-4 · Cited by 22
A visual attention-based keyword extraction for document classification
Wu, X., Du, Z., & Guo, Y. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-5788-9 · Cited by 23
Deep blind image quality assessment by employing FR-IQA
Kim, J., & Lee, S. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296869 · Cited by 24
Embedded Vision Systems: A Review of the Literature
Bhowmik, D., & Appiah, K. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-78890-6_17 · Cited by 25
Adaptive Lightweight Regularization Tool for Complex Analytics
Luo, Z., Cai, S., Gao, J., et al. (2018) · Proceedings - International Conference on Data Engineering · doi:10.1109/icde.2018.00051 · Cited by 22
Badejo, J. A., Adetiba, E., Akinrinmade, A. A., & Akanle, M. B. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-78759-6_25 · Cited by 23
Deep neural network ensemble architecture for eye movements classification
Arsenović, M., Sladojević, S., Stefanović, D., et al. (2018) · International Symposium INFOTEH-JAHORINA (INFOTEH) · doi:10.1109/infoteh.2018.8345537 · Cited by 22
Awiszus, M., & Rosenhahn, B. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00293 · Cited by 23
Bramhe, V. S., Ghosh, S. K., & Garg, P. K. (2018) · 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-xlii-3-79-2018 · Cited by 22
Decision-theoretic model to identify printed sources
Tsai, M.-J., Yuadi, I., & Tao, Y.-H. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-5938-0 · Cited by 21
Deep learning based smart radar vision system for object recognition
Wen, Z., Liu, D., Liu, X., et al. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-0853-9 · Cited by 22
Deep Learning Frameworks on Apache Spark: A Review
Venkatesan, N. J., Nam, C.-S., & Shin, D. R. (2018) · IETE Technical Review · doi:10.1080/02564602.2018.1440975 · Cited by 22
MPNET: An End-to-End Deep Neural Network for Object Detection in Surveillance Video
Wang, H., Wang, P., & Qian, X. (2018) · IEEE Access · doi:10.1109/access.2018.2836921 · Cited by 21
The use of deep learning to automate the segmentation of the skeleton from CT volumes of pigs1
Kvam, J., Gangsei, L. E., Kongsro, J., & Solberg, A. H. S. (2018) · Translational Animal Science · doi:10.1093/tas/txy060 · Cited by 25
Handwritten tifinagh character recognition using deep learning architectures
Sadouk, L., Gadi, T., & Essoufi, E. H. (2017) · doi:10.1145/3109761.3109788 · Cited by 23
End-to-end soccer video scene and event classification with deep transfer learning
Hong, Y., Ling, C., & Ye, Z. (2018) · International Conference on Intelligent Systems and Computer Vision (ISCV) · doi:10.1109/isacv.2018.8369043 · Cited by 24
Ensemble Network for Region Identification in Breast Histopathology Slides
Marami, B., Prastawa, M. W., Chan, M. S. M., Donovan, M., Fernandez, G., & Zeineh, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93000-8_98 · Cited by 25
Chen, X., Chen, Q., Tian, M., Ji, M., Zou, C., Wang, X., & Wang, B. (2017) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads.2017.00034 · Cited by 20
Uncovering Scene Context for Predicting Privacy of Online Shared Images
Tonge, A., Caragea, C., & Squicciarini, A. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12180 · Cited by 22
Modeling coverage with semantic embedding for image caption generation
Jiang, T., Zhang, Z., & Yang, Y. (2018) · The Visual Computer · doi:10.1007/s00371-018-1565-z · Cited by 21
Roy, P., Song, S. L., Krishnamoorthy, S., Vishnu, A., Sengupta, D., & Liu, X. (2018) · ACM Transactions on Architecture and Code Optimization · doi:10.1145/3199605 · Cited by 22
A pointer network based deep learning algorithm for 0–1 Knapsack Problem
Gu, S., & Hao, T. (2018) · International Conference on Advanced Computational Intelligence (ICACI) · doi:10.1109/icaci.2018.8377505 · Cited by 24
Maeda, K., Takahashi, S., Ogawa, T., & Haseyama, M. (2018) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2018.2849593 · Cited by 22
IBM POWER9 and cognitive computing
Kumar, M., Horn, W., Kepner, J., Moreira, J. E., & Pattnaik, P. (2018) · IBM Journal of Research and Development · doi:10.1147/jrd.2018.2846958 · Cited by 22
Liu, Z., Li, L., Xu, H., et al. (2018) · International Conference on Electronics Technology (ICET) · doi:10.1109/eltech.2018.8401477 · Cited by 22
Deep Learning of Path-Based Tree Classifiers for Large-Scale Plant Species Identification
Zhang, H., He, G., Peng, J., Kuang, Z., & Jianping Fan, B. (2018) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr.2018.00013 · Cited by 25
Deep Learning as a Frontier of Machine Learning: A Review
Kumar, V., & L., M. (2018) · International Journal of Computer Applications · doi:10.5120/ijca2018917433 · Cited by 25
Fall monitoring and detection for at-risk persons using a UAV
Iuga, C., Drăgan, P., & Buşoniu, L. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.06.262 · Cited by 22
Local morphological pattern: A scale space shape descriptor for texture classification
Roy, S. K., Chanda, B., Chaudhuri, B. B., et al. (2018) · Digital Signal Processing · doi:10.1016/j.dsp.2018.06.016 · Cited by 22
Semi-supervised spatio-temporal CNN for recognition of surgical workflow
Chen, Y., Sun, Q. L., & Zhong, K. (2018) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-018-0316-4 · Cited by 21
An Asian Face Dataset and How Race Influences Face Recognition
Xiong, Z., Wang, Z., Du, C., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00767-6_35 · Cited by 22
Weighted Generalized Mean Pooling for Deep Image Retrieval
Wu, X., Irie, G., Hiramatsu, K., & Kashino, K. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451317 · Cited by 24
A Deep Learning Based Pipeline for Image Grading of Diabetic Retinopathy
Wang, Y., Wang, G. A., Fan, W., & Li, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03649-2_24 · Cited by 23
Learning Based Single Image Blur Detection and Segmentation
Purohit, K., Shah, A., & Rajagopalan, A. N. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451765 · Cited by 22
Xian, Y., & Hu, H. (2018) · IET Computer Vision · doi:10.1049/iet-cvi.2018.5103 · Cited by 22
Adult Image Classification by a Local-Context Aware Network
Wang, X., Cheng, F., Wang, S., Sun, H., Liu, G., & Zhou, C. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451366 · Cited by 23
Jeong, Y., Lee, S., Park, D., & Park, K. H. (2018) · Symmetry · doi:10.3390/sym10090385 · Cited by 24
Heterogeneity in chromatic distance in images and characterization of massive painting data set
Lee, B., Kim, D., Sun, S., Jeong, H., & Park, J. M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0204430 · Cited by 22
Hassan, T., Akram, M. U., Akhtar, M., Khan, S. A., & Yasin, U. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-1078-3 · Cited by 20
Gao, F., Ge, Y., Lu, S., & Zhang, Y. (2018) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2018.5197 · Cited by 21
Mansoori, S. A., Kunhu, A., & Al-Ahmad, H. (2018) · doi:10.1117/12.2325732 · Cited by 22
Khan, A., Sung, J. E., & Kang, J. (2018) · Information Fusion · doi:10.1016/j.inffus.2018.10.008 · Cited by 22
Optic Disc and Cup Segmentation with Blood Vessel Removal from Fundus Images for Glaucoma Detection
Jiang, Y., Xia, H., Xu, Y., Cheng, J. T., Fu, H., Duan, L., Meng, Z., & Liu, J. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512400 · Cited by 25
A Model-Based Approach to Streamlining Distributed Training for Asynchronous SGD
Lin, S.-H., Paolieri, M., Chou, C., & Golubchik, L. (2018) · Proceedings - International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems · doi:10.1109/mascots.2018.00037 · Cited by 23
Kyriazis, A. D., Noroozizadeh, S., Refaee, A., Choi, W., Chu, L.-T., Bashir, A., Cheng, W. H., Zhao, R., Namjoshi, D., Salcudean, S. E., et al. (2018) · Neuroinformatics · doi:10.1007/s12021-018-9405-x · Cited by 20
Behavior and Personality Analysis in a Nonsocial Context Dataset
Dotti, D., Popa, M. C., & Asteriadis, S. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00312 · Cited by 22
Semantic image segmentation via guidance of image classification
Shen, F., & Zeng, G. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.11.027 · Cited by 22
NU-Net: Deep Residual Wide Field of View Convolutional Neural Network for Semantic Segmentation
Samy, M., Amer, K., Eissa, K., Shaker, M., & Elhelw, M. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00050 · Cited by 24
Attention-based Neural Network for Traffic Sign Detection
Zhang, J., Hui, L., Lu, J., & Zhu, Y. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8546289 · Cited by 22
ViPED: On-road vehicle passenger detection for autonomous vehicles
Amanatiadis, A. A., Karakasis, E., Bampis, L., Ploumpis, S., & Gasteratos, A. C. (2018) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2018.12.002 · Cited by 22
Khorasani, F., Esfeden, H. A., Abu‐Ghazaleh, N., & Sarkar, V. (2018) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1109/micro.2018.00038 · Cited by 22
On medical application of neural networks trained with various types of data
Karako, K., Chen, Y., & Tang, W. (2018) · BioScience Trends · doi:10.5582/bst.2018.01264 · Cited by 21
Deep Reactive Policies for Planning in Stochastic Nonlinear Domains
Bueno, T. P., de Barros, L. N., Mauá, D. D., & Sanner, S. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33017530 · Cited by 22
Gordon, M. N., Hadjiiski, L. M., Cha, K. H., et al. (2018) · Medical Physics · doi:10.1002/mp.13326 · Cited by 22
Character and Text Recognition of Khmer Historical Palm Leaf Manuscripts
Valy, D., Verleysen, M., Chhun, S., & Burie, J.-C. (2018) · International Conference on Frontiers in Handwriting Recognition · doi:10.1109/icfhr-2018.2018.00012 · Cited by 27
Learning to Fuse Multiscale Features for Visual Place Recognition
Mao, J., Hu, X., He, X., Zhang, L., Wu, L., & Milford, M. J. (2018) · IEEE Access · doi:10.1109/access.2018.2889030 · Cited by 19
Biological image analysis using deep learning-based methods: Literature review
Wang, H., Shang, S., Long, L., Hu, R., Wu, Y., Chen, N., Zhang, S., Cong, F., & Lin, S. (2018) · Digital Medicine · doi:10.4103/digm.digm_16_18 · Cited by 21
Computer-aided grading of prostate cancer from MRI images using Convolutional Neural Networks
Abraham, B., & Nair, P. (. ). M. S. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-169913 · Cited by 23
Classification of anti-submarine warfare sonar targets using a deep neural network
Berg, H., & Hjelmervik, K. T. (2018) · OCEANS 2018 MTS/IEEE Charleston · doi:10.1109/oceans.2018.8604847 · Cited by 22
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
