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 59,001–59,100 of 108,438
NEOL: Toward Never-Ending Object Learning for robots
Sun, Y., & Fox, D. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487302 · Cited by 7
Homemade TS-Net for Automatic Face Recognition
Lin, S., Zhao, Z., & Su, F. (2016) · International Conference on Multimedia Retrieval · doi:10.1145/2911996.2911999 · Cited by 8
Multimedia Big Data: Content Analysis and Retrieval
Hayes, J. (2015) · Springer eBooks · doi:10.1007/978-3-319-25313-8_3 · Cited by 7
A spectral filtering based deep learning for detection of logo and stamp
Nandedkar, A. V., Mukherjee, J., & Sural, S. (2015) · National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) · doi:10.1109/ncvpripg.2015.7490053 · Cited by 7
A computational framework for disease grading using protein signatures
Zerhouni, E., Prisacari, B., Zhong, Q., Wild, P. J., & Gabrani, M. (2016) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493529 · Cited by 6
Kalantidis, Y., Kennedy, L. S., Nguyen, H. H., Mellina, C., & Shamma, D. A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46604-0_49 · Cited by 8
RETRACTED: Automatic online vision-based inspection system of coupler yoke for freight trains
Zheng, C., & Wei, Z. (2016) · Journal of Electronic Imaging · doi:10.1117/1.jei.25.6.061602 · Cited by 6
RetractedAction Recognition in Surveillance Video Using ConvNets and Motion History Image
Luo, S., Yang, H., Wang, C., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-44781-0_23 · Cited by 7
Sequential Labeling with Online Deep Learning: Exploring Model Initialization
Chen, G., Xu, R., & Srihari, S. N. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46227-1_48 · Cited by 7
Preserving local spatial information in image similarity using tensor aggregation of local features
Picard, D. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532347 · Cited by 7
Summit-Training: A hybrid Semi-Supervised technique and its application to classification tasks
Tencer, L., Režnáková, M., & Cheriet, M. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.06.008 · Cited by 7
Video Affective Content Analysis Based on Protagonist via Convolutional Neural Network
Zhu, Y., Jiang, Z., Peng, J., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48890-5_17 · Cited by 7
A benchmark for cross-weather traffic scene understanding
Di, S., Zhang, H., Mei, X., Prokhorov, D. V., & Ling, H. (2016) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2016.7795904 · Cited by 8
Store classification using Text-Exemplar-Similarity and Hypotheses-Weighted-CNN
Huang, C., Li, H., Li, W., Wu, Q., & Xu, L. (2017) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2017.01.011 · Cited by 6
Classification of MRI data using deep learning and Gaussian process-based model selection
Bertrand, H., Perrot, M., Ardon, R., et al. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950626 · Cited by 7
Analog signal processing solution for machine vision applications
Athreyas, N., Gupta, D. V., & Gupta, J. (2017) · Journal of Real-Time Image Processing · doi:10.1007/s11554-017-0669-4 · Cited by 7
Nahar, S., & Joshi, M. V. (2017) · IPSJ Transactions on Computer Vision and Applications · doi:10.1186/s41074-016-0013-0 · Cited by 7
Lucene and deep learning based commodity information analysis system
Cao, J., Lin, J., Wu, S., et al. (2016) · IEEE International Conference on Consumer Electronics - China (ICCE-China) · doi:10.1109/icce-china.2016.7849764 · Cited by 7
Linear guided autoencoder: Representation learning with linearity
Ohno, H. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.02.019 · Cited by 7
End-to-end binary representation learning via direct binary embedding
Liu, L., Rahimpour, A., Taalimi, A., & Qi, H. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296483 · Cited by 9
High-Order Local Pooling and Encoding Gaussians Over a Dictionary of Gaussians
Li, P., Zeng, H., Wang, Q., Shiu, S., & Zhang, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2695884 · Cited by 7
A Hierarchical Convolutional Neural Network for vesicle fusion event classification
Li, H., Mao, Y., Yin, Z., et al. (2017) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2017.04.003 · Cited by 7
Facial expression recognition by re-ranking with global and local generic features
Vo, D. M., Sugimoto, A., & Lê, T. H. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900279 · Cited by 7
Relief R-CNN: Utilizing Convolutional Features for Fast Object Detection
Li, G., Liu, J., Jiang, C., Zhang, L., Lin, M., & Tang, K. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59072-1_46 · Cited by 9
Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions
Katevas, K., Leontiadis, I., Pielot, M., & Serrà, J. (2017) · doi:10.1145/3089801.3089802 · Cited by 16
Enriched dermoscopic-structure-based cad system for melanoma diagnosis
López-Labraca, J., Fernández‐Torres, M., González-Díaz, I., et al. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4879-3 · Cited by 7
Deep-net fusion to classify shots in concert videos
Wei, W.-L., Lin, J., Liu, T.-L., Yang, Y., Wang, H., Tyan, H.-R., & Liao, H.-Y. M. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952383 · Cited by 7
Zaourar, L., Aba, M. A., Briand, D., & Philippe, J. (2017) · IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · doi:10.1109/ipdpsw.2017.123 · Cited by 7
Roth, H. R., Lü, L., Liu, J., Yao, J., Seff, A., Cherry, K. M., Kim, L., & Summers, R. M. (2017) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-42999-1_3 · Cited by 8
Xue, D.-X., Zhang, R., Zhao, Y., et al. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2282000 · Cited by 7
3D convolutional object recognition using volumetric representations of depth data
Çağlayan, A., & Can, A. B. (2017) · doi:10.23919/mva.2017.7986817 · Cited by 7
Understanding aesthetics in photography using deep convolutional neural networks
Suchecki, M., & Trzcinski, T. P. (2017) · Signal Processing: Algorithms, Architectures, Arrangements, and Applications · doi:10.23919/spa.2017.8166855 · Cited by 8
Multimodal News Article Analysis
Ramisa, A. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/737 · Cited by 7
Juefei-Xu, F., Verma, E., & Savvides, M. (2017) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-61657-5_8 · Cited by 7
CNN-Based Background Subtraction for Long-Term In-Vial FIM Imaging
Scherzinger, A., Klemm, S., Berh, D., & Jiang, X. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-64689-3_29 · Cited by 7
Learning Affine Hull Representations for Multi-Shot Person Re-Identification
Karanam, S., Wu, Z., & Radke, R. J. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2732822 · Cited by 7
Infer Cause of Death for Population Health Using Convolutional Neural Network
Wu, H., & Wang, M. D. (2017) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3107411.3107447 · Cited by 6
Umehara, K., Ota, J., Ishimaru, N., et al. (2017) · Open Journal of Medical Imaging · doi:10.4236/ojmi.2017.73010 · Cited by 7
To Veer or Not to Veer: Learning from Experts How to Stay Within the Crosswalk
Diaz, M., Girgis, R., Fevens, T., & Cooperstock, J. R. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.174 · Cited by 7
CNN Based Transfer Learning for Scene Script Identification
Tounsi, M., Moalla, I., Lebourgeois, F., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70136-3_74 · Cited by 7
The Effect of Task Similarity on Deep Transfer Learning
Zhang, W., Fang, Y., & Ma, Z. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70096-0_27 · Cited by 7
Local Deep Descriptors in Bag-of-Words for Image Retrieval
Cao, J., Huang, Z. H., & Shen, H. T. (2017) · Thematic Workshops of ACM Multimedia · doi:10.1145/3126686.3127018 · Cited by 8
Obfuscated image classification for secure image-centric friend recommendation
Ahmed, K. W., Chanda, O., Mohammed, N., et al. (2017) · Sustainable Cities and Society · doi:10.1016/j.scs.2017.10.001 · Cited by 7
Yan, Z., Feng, Y., Cheng, C., Fu, J., Zhou, X., & Yuan, J. (2017) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2017.0066 · Cited by 7
Complex crowdsourcing task allocation strategies employing supervised and reinforcement learning
Cui, L., Zhao, X., Liu, L., Yu, H., & Miao, Y. (2017) · International Journal of Crowd Science · doi:10.1108/ijcs-08-2017-0011 · Cited by 9
Siamese Cosine Network Embedding for Person Re-identification
Wang, J., Li, Y., & Miao, Z. (2017) · Communications in computer and information science · doi:10.1007/978-981-10-7305-2_31 · Cited by 7
Detection of abnormal candidate regions on temporal subtraction images based on DCNN
Nagao, M., Miyake, N., Yoshino, Y., et al. (2017) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas.2017.8204218 · Cited by 7
Handwritten recognition of Tamil vowels using deep learning
Prashanth, N., Siddarth, B. G. R., Ganesh, A., & Kumar, V. N. (2017) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/263/5/052035 · Cited by 7
Automatic melody extraction algorithm using a convolutional neural network
Lee, J., Jang, D., & Yoon, K. (2017) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2017.12.019 · Cited by 7
Multi-Modal Automatic Target Recognition for Anti-Ship Missiles with Imaging Infrared Capabilities
Kechagias‐Stamatis, O., Aouf, N., & Nam, D. (2017) · Sensor Signal Processing for Defence Conference (SSPD) · doi:10.1109/sspd.2017.8233244 · Cited by 7
Iris segmentation using deep neural networks
Sinha, N., Joshi, A., Gangwar, A. K., et al. (2017) · International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct.2017.8226190 · Cited by 7
Automatic Image Cropping and Selection Using Saliency: An Application to Historical Manuscripts
Cornia, M., Pini, S., Baraldi, L., et al. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-73165-0_17 · Cited by 7
Hunting for naval mines with deep neural networks
Gebhardt, D., Parikh, K., Dzieciuch, I., et al. (2017) · OCEANS 2017 – Anchorage · Cited by 16
Yamanoi, Y., & Kato, R. (2017) · IEEE International Conference on Cyborg and Bionic Systems (CBS) · doi:10.1109/cbs.2017.8266079 · Cited by 7
A convolutional neural network approach to parking monitoring in urban radar sensing
Martínez, J., Zoeke, D., & Vossiek, M. (2017) · European Radar Conference (EuRAD) · doi:10.23919/eurad.2017.8249153 · Cited by 7
Deep feature learning for person re-identification in a large-scale crowdsourced environment
Oh, S. H., Han, S.-W., Choi, B., et al. (2018) · The Journal of Supercomputing · doi:10.1007/s11227-017-2221-5 · Cited by 7
Automatic Access Control Based on Face and Hand Biometrics in a Non-cooperative Context
Jahromi, M. N. S., Bonderup, M. B., Asadi, M., Avots, E., Nasrollahi, K., Escalera, S., Shohreh Kasaei, B., Moeslund, T. B., & Anbarjafari, G. (2018) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw.2018.00009 · Cited by 7
Part-based convolutional neural network for visual recognition
Yang, L., Xie, X., Li, P., Zhang, D. D., & Zhang, L. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296586 · Cited by 8
A hybrid deep learning approach to predict malignancy of breast lesions using mammograms
Gong, J., Zheng, B., Wang, Y., Heidari, M., Mirniaharikandehei, S., Qian, W., & Qiu, Y. (2018) · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · doi:10.1117/12.2286555 · Cited by 8
A Survey on Sentiment Classification in Face Recognition
Qian, J. (2018) · Journal of Physics Conference Series · doi:10.1088/1742-6596/960/1/012030 · Cited by 7
Fast multi-object tracking using convolutional neural networks with tracklets updating
Zhang, Y., Tang, Y., Fang, B., et al. (2017) · International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) · doi:10.1109/spac.2017.8304296 · Cited by 7
Exploring the influence of feature representation for dictionary selection based video summarization
Ma, M., Mei, S., Ji, J., et al. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296815 · Cited by 7
Deep learning—Accelerating Next Generation Performance Analysis Systems?
Brock, H. (2018) · Conference of the International Sports Engineering Association (ISEA) · doi:10.3390/proceedings2060303 · Cited by 7
Face recognition by landmark pooling-based CNN with concentrate loss
Huang, R., Xie, X., Feng, Z., et al. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296548 · Cited by 7
Kim, S., Park, J., Bang, J., & Lee, H. (2018) · Augmented Human International Conference · doi:10.1145/3174910.3174922 · Cited by 8
Combining Word Order and CNN-LSTM for Sentence Sentiment Classification
Shuang, K., Ren, X., Chen, J., Shan, X., & Xu, P. (2017) · International Conference on Software and e-Business (ICSEB) · doi:10.1145/3178212.3178230 · Cited by 7
Exploiting Ladder Networks for Gene Expression Classification
Gölcük, G., Tuncel, A., & Canakoglu, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-78723-7_23 · Cited by 7
Trigonometric series in orthogonal expansions for density estimates of deep image features
Savchenko, A. (2018) · Computer Optics · doi:10.18287/2412-6179-2018-42-1-149-158 · Cited by 7
Peng, K.-Y., Fu, S., Yu-ping, L., & Hsu, W. (2017) · International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation · doi:10.1109/samos.2017.8344617 · Cited by 8
NN compactor: Minimizing memory and logic resources for small neural networks
Hong, S., Lee, I., & Park, Y. (2018) · Design, Automation, and Test in Europe · doi:10.23919/date.2018.8342074 · Cited by 7
Perceiving Commerial Activeness Over Satellite Images
He, Z., Yang, S., Zhang, W., & Zhang, J. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3186353 · Cited by 9
Facial Expression Recognition Using a Large Out-of-Context Dataset
Tran, E., Mayhew, M. B., Kim, H., Karande, P., & Kaplan, A. D. (2018) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw.2018.00012 · Cited by 6
Albaqsami, A., Hosseini, M. S., & Bagherzadeh, N. (2018) · Design, Automation, and Test in Europe · doi:10.23919/date.2018.8342031 · Cited by 6
Deep Domain Adaptation Based on Multi-layer Joint Kernelized Distance
Mao, S., Shen, X. X., & Chung, F.-L. (2018) · doi:10.1145/3209978.3210155 · Cited by 7
Learning spatio-temporal context via hierarchical features for visual tracking
Cao, Y., Ji, H., Zhang, W., & Xue, F. (2018) · Signal Processing Image Communication · doi:10.1016/j.image.2018.04.010 · Cited by 7
MALMM: A multi-array architecture for large-scale matrix multiplication on FPGA
Huang, Y. R., Shen, J., Qiao, Y., et al. (2018) · IEICE Electronics Express · doi:10.1587/elex.15.20180286 · Cited by 7
Updating spatial working memory in a dynamic visual environment
Boon, P. J., Theeuwes, J., & Belopolsky, A. V. (2019) · Cortex · doi:10.1016/j.cortex.2019.04.021 · Cited by 6
Sentiment Classification with Convolutional Neural Network using Multiple Word Representations
Ju, H., & Yu, H. (2018) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1145/3164541.3164610 · Cited by 7
From MRFS to CNNS: A novel image restoration method
Lan, X., & Barner, K. E. (2018) · Conference on Information Sciences and Systems · doi:10.1109/ciss.2018.8362260 · Cited by 7
Evolutionary image simplification for lung nodule classification with convolutional neural networks
Lückehe, D., & von Voigt, G. (2018) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-018-1794-7 · Cited by 7
Nguyen, S. P., Li, Z., & Shang, Y. (2017) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2017.00086 · Cited by 7
Enhancing Interior and Exterior Deep Facial Features for Face Detection in the Wild
Zhu, C., Zheng, Y., Luu, K., & Savvides, M. (2018) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2018.00041 · Cited by 6
Photographic image synthesis with improved U-net
Liu, G., Si, J., Hu, Y., & Li, S. (2018) · International Conference on Advanced Computational Intelligence (ICACI) · doi:10.1109/icaci.2018.8377492 · Cited by 7
Classifying Neuromorphic Data Using a Deep Learning Framework for Image Classification
Gopalakrishnan, R., Chua, Y., & Iyer, L. (2018) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv.2018.8581256 · Cited by 8
Evolving indirectly encoded convolutional neural networks to play tetris with low-level features
Schrum, J. (2018) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3205455.3205459 · Cited by 7
iPand: Accurate Gesture Input with Smart Acoustic Sensing on Hand
Shumin, C., Yang, P., Li, X., Chen, M., & Zhu, P. (2018) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/sahcn.2018.8397157 · Cited by 7
Optimization of deep network models through fine tuning
Wani, M. A., & Afzal, S. (2018) · International Journal of Intelligent Computing and Cybernetics · doi:10.1108/ijicc-06-2017-0070 · Cited by 7
Symmetric Power Activation Functions for Deep Neural Networks
Berradi, Y. (2018) · doi:10.1145/3230905.3230956 · Cited by 7
HyConv: Accelerating Multi-Phase CNN Computation by Fine-Grained Policy Selection
Li, X., Zhang, G., Wang, Z., & Zheng, W. (2018) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2018.2864299 · Cited by 7
Automatic Localization of Optic Disc using Modified U-Net
Gu, Z., Jiang, S., Lee, J., et al. (2018) · doi:10.1145/3232651.3232671 · Cited by 7
Multiple Perspective Object Tracking via Context-Aware Correlation Filter
Zhou, X., Li, J., Chen, S., et al. (2018) · IEEE Access · doi:10.1109/access.2018.2861824 · Cited by 7
Deep Convolutional Network Based on Pyramid Architecture
Lv, E., Wang, X., Cheng, Y., & Yu, Q. (2018) · IEEE Access · doi:10.1109/access.2018.2860785 · Cited by 7
Hierarchical residual stochastic networks for time series recognition
Xie, C., Li, C., Zhang, B., Pan, L., Ye, Q., & Chen, W. R. (2018) · Information Sciences · doi:10.1016/j.ins.2018.08.065 · Cited by 7
Fully Convolutional Neural Networks for Tissue Histopathology Image Classification and Segmentation
Peng, B., Chen, L., Shang, M., & Xu, J. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451517 · Cited by 7
Computational Texture and Patterns
Dana, K. (2018) · Synthesis lectures on computer vision · doi:10.2200/s00819ed1v01y201712cov014 · Cited by 12
Task management on fully heterogeneous micro‐server system: Modeling and resolution strategies
Zaourar, L., Aba, M. A., Briand, D., & Philippe, J. (2018) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.4798 · Cited by 7
Robust Facial Pose Estimation Using Landmark Selection Method for Binocular Stereo Vision
Park, J., Heo, S., Lee, K., Song, H., & Lee, S. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451443 · Cited by 7
Learning 3D Scene Semantics and Structure from a Single Depth Image
Yang, B., Lai, Z., Lu, X., et al. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00069 · Cited by 7
CYLINDRICAL TRANSFORM: 3D SEMANTIC SEGMENTATION OF KIDNEYS WITH LIMITED ANNOTATED IMAGES
Salehinejad, H., Naqvi, S., Colak, E., Barfett, J., & Valaee, S. (2018) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2018.8646668 · Cited by 8
Deep Discriminative Clustering Network
Shaol, X., Ge, K., Su, H., Luo, L., Peng, B., & Li, D. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489417 · Cited by 7
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
