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 37,101–37,200 of 108,435
Lin, J., Chen, X., Cai, J., et al. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.108342 · Cited by 20
Cai, Z., Qiao, X., Zhang, J., Feng, Y., Hu, X., & Jiang, N. (2023) · Applied Sciences · doi:10.3390/app132111925 · Cited by 20
GUI-Based Software Testing: An Automated Approach Using GPT-4 and Selenium WebDriver
Zimmermann, D., & Koziolek, A. (2023) · IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW) · doi:10.1109/asew60602.2023.00028 · Cited by 25
RETRACTED: Lyme rashes disease classification using deep feature fusion technique
Ali, G., Anwar, M., Nauman, M., Faheem, M., & Rashid, J. (2023) · Skin Research and Technology · doi:10.1111/srt.13519 · Cited by 23
RetractedGenerative AI Model for Artistic Style Transfer Using Convolutional Neural Networks
Miah, J., Cao, D. M., Sayed, M. A., et al. (2023) · Journal of Computer Science and Technology Studies · doi:10.32996/jcsts.2023.5.4.9 · Cited by 21
Enhanced multi-scale networks for semantic segmentation
Li, T., Cui, Z., Yu, H., Li, G., Li, M., & Wei, D. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01279-x · Cited by 20
Pham, T.-D., Duong, M.-T., Ho, Q.-T., Lee, S., & Hong, M.-C. (2023) · Sensors · doi:10.3390/s23249658 · Cited by 23
Improving wind speed forecasting at Adama wind farm II in Ethiopia through deep learning algorithms
Chaka, M. D., Semie, A. G., Mekonnen, Y. S., Geffe, C. A., Kebede, H., Mersha, Y., Anose, F. A., & Benti, N. E. (2023) · Case Studies in Chemical and Environmental Engineering · doi:10.1016/j.cscee.2023.100594 · Cited by 21
Urban Traffic Flow Prediction Using LSTM and GRU
Jang, H.-C., & Chen, C.-A. (2024) · IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS) · doi:10.3390/engproc2023055086 · Cited by 20
Chen, G., Liang, Y., Li, S., & Xu, Z. (2024) · Applied Mathematical Modelling · doi:10.1016/j.apm.2023.12.018 · Cited by 24
CR-CAM: Generating explanations for deep neural networks by contrasting and ranking features
Li, Y., Liang, H., Zheng, H., & Yu, R. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110251 · Cited by 22
FDAN: Fuzzy deep attention networks for driver behavior recognition
Xiao, W., Xie, G., Liu, H., et al. (2024) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2023.103063 · Cited by 20
CrackHAM: A Novel Automatic Crack Detection Network Based on U-Net for Asphalt Pavement
He, M., & Lau, T. L. (2024) · IEEE Access · doi:10.1109/access.2024.3353729 · Cited by 23
HOSNeRF: Dynamic Human-Object-Scene Neural Radiance Fields from a Single Video
Liu, J., Cao, Y., Yang, T., Xu, Z., Keppo, J., Shan, Y., Qie, X., & Shou, M. Z. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01694 · Cited by 21
Interactive content-based image retrieval with deep learning for CT abdominal organ recognition
Lo, C., Wang, C.-C., & Hung, P. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad1f86 · Cited by 20
Toward real-time fracture detection on image logs using deep convolutional neural network YOLOv5
Olya, B. A. M., Mohebian, R., Bagheri, H., et al. (2024) · Interpretation · doi:10.1190/int-2022-0104.1 · Cited by 20
Classification of Adventitious Sounds Combining Cochleogram and Vision Transformers
Mang, L. D., Martínez, F. D. G., Martínez-Muñoz, D., et al. (2024) · Sensors · doi:10.3390/s24020682 · Cited by 20
Multi input–Multi output 3D CNN for dementia severity assessment with incomplete multimodal data
Gravina, M., Garcia-Pedrero, A. M., Gonzalo‐Martín, C., Sansone, C., & Soda, P. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102774 · Cited by 23
MSE-Net: A novel master–slave encoding network for remote sensing scene classification
Yue, H., Qing, L., Zhang, Z., Wang, Z., Guo, L., & Peng, Y. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.107909 · Cited by 22
Q-RBSA: high-resolution 3D EBSD map generation using an efficient quaternion transformer network
Jangid, D. K., Brodnik, N. R., Echlin, M. P., Gudavalli, C., Levenson, C., Pollock, T. M., Daly, S. H., & Manjunath, B. S. (2024) · npj Computational Materials · doi:10.1038/s41524-024-01209-6 · Cited by 18
Alam, S. S., Shuvo, S. B., Ali, S. N., Ahmed, F., Chakma, A., & Jang, Y. M. (2024) · IEEE Access · doi:10.1109/access.2024.3359701 · Cited by 25
A lightweight convolutional neural network for automated crack inspection
Chang, S., & Zheng, B. (2024) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2024.135151 · Cited by 21
LKFormer: large kernel transformer for infrared image super-resolution
Qin, F., Yan, K., Wang, C., Ge, R., Peng, Y. K., & Zhang, K. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18409-3 · Cited by 20
CDYL for infrared and visible light image dense small object detection
Wu, H., Zhu, Y., & Li, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-54146-1 · Cited by 23
Li, L., Yu, M., Li, X., et al. (2024) · Analytical Methods · doi:10.1039/d3ay02250a · Cited by 20
Maragheh, H. K., Gharehchopogh, F. S., Majidzadeh, K., & Sangar, A. B. (2024) · Neural Processing Letters · doi:10.1007/s11063-024-11500-8 · Cited by 28
Exploration of AI-powered DenseNet121 for effective diabetic retinopathy detection
Lakshmi, K. S., & Sargunam, B. (2024) · International Ophthalmology · doi:10.1007/s10792-024-03027-7 · Cited by 20
Abouelmagd, L. M., Dahy, G., Farrag, T. A., Darwish, A., & Hassnien, A. E. (2024) · International Journal of Information Technology · doi:10.1007/s41870-023-01713-w · Cited by 21
A Survey of Offline- and Online-Learning-Based Algorithms for Multirotor Uavs
Sönmez, S., Rutherford, M. J., & Valavanis, K. P. (2024) · Drones · doi:10.3390/drones8040116 · Cited by 20
The rise of AI: a comprehensive research review
Padmaja, C. V. R., & Lakshminarayana, S. (2024) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v13.i2.pp2226-2235 · Cited by 35
Choi, C.-W., & Kim, J. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111739 · Cited by 21
Wang, L., Qi, Y., Mathiopoulos, P. T., Zhao, C., & Mazhar, S. (2024) · Remote Sensing · doi:10.3390/rs16071299 · Cited by 22
Xian, R., Wang, X., & Manocha, D. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00649 · Cited by 24
Guo, W., Fu, Z., Bu, T., Wang, Z., Fang, H., Zhou, S., Huang, J., & Wu, H. (2024) · Nano Energy · doi:10.1016/j.nanoen.2024.109639 · Cited by 20
A Beginner’s Guide to Artificial Intelligence for Ophthalmologists
Kang, D., Wu, H., Yuan, L., Shi, Y., Jin, K., & Grzybowski, A. E. (2024) · Ophthalmology and Therapy · doi:10.1007/s40123-024-00958-3 · Cited by 20
Effects of ChatGPT on Korean EFL Learners’ Main-Idea Reading Comprehension via Top-Down Processing
Kim, R. (2024) · 어학연구 · doi:10.30961/lr.2024.60.1.83 · Cited by 34
ERNet: A Rapid Road Crack Detection Method Using Low-Altitude UAV Remote Sensing Images
Duan, Z., Liu, J., Ling, X., et al. (2024) · Remote Sensing · doi:10.3390/rs16101741 · Cited by 20
Research on image recognition of tomato leaf diseases based on improved AlexNet model
Qiu, J., Lu, X., Wang, X., et al. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e33555 · Cited by 20
Chen, Z. (2024) · Entropy · doi:10.3390/e26070560 · Cited by 21
Jiang, S., Shuai, X., & Xing, G. (2024) · ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1109/ipsn61024.2024.00007 · Cited by 23
Current Trends on Deep Learning Techniques Applied in Iron and Steel Making Field: A Review
Tsutsui, K., Namba, T., Kihara, K., Hirata, J., Matsuo, S., & Ito, K. (2024) · ISIJ International · doi:10.2355/isijinternational.isijint-2024-098 · Cited by 23
Xiong, Y., Liu, Z., Tan, J., & Hao, L. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102712 · Cited by 20
Carbon emissions forecasting based on tensor decomposition with multi-source data fusion
Xu, X., Cao, X., & Yu, L. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121235 · Cited by 20
Ding, K., Feng, X., & Yu, H. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112317 · Cited by 20
Network model with internal complexity bridges artificial intelligence and neuroscience
He, L., Xu, Y., He, W., Lin, Y., Tian, Y., Wu, Y., Wang, W., Zhang, Z., Han, J., Tian, Y., et al. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00674-9 · Cited by 23
Xiong, B., Hong, R., Wang, J., Li, W., Zhang, J., Lv, S., & Ge, D. (2024) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2024.138206 · Cited by 22
A Hybrid Embedding for Generalized Zero-Shot Scene Classification in Remote Sensing Images
Rambabu, D., Datla, R., Chalavadi, V., & Mohan, C. K. (2024) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss61716.2024.10672594 · Cited by 21
Luo, X., Liu, D., Kong, H., Huai, S., Chen, H., Xiong, G., & Liu, W. (2024) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3701728 · Cited by 27
A Survey on Deep Learning for Few-Shot PolSAR Image Classification
Wang, N., Jin, W., Bi, H., Chen, X., & Gao, J. (2024) · Remote Sensing · doi:10.3390/rs16244632 · Cited by 25
Li, Y., Li, L., Yuan, H., He, K., Chen, H., Xie, J., Wang, B., & Wang, X. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.125179 · Cited by 21
Artificial intelligence streamlines scientific discovery of drug–target interactions
Yang, Y., & Cheng, F. (2025) · British Journal of Pharmacology · doi:10.1111/bph.17427 · Cited by 22
Zaidi, S. A., Chouvatut, V., Phongnarisorn, C., & Praserttitipong, D. (2025) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2025.100230 · Cited by 26
Fine-grained classification of identity document types with only one example
Simon, M., Rodner, E., & Denzler, J. (2015) · Machine Vision Applications · doi:10.1109/mva.2015.7153149 · Cited by 20
Zheng, D., Hu, T., You, Q., et al. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v9i1.14591 · Cited by 20
Attribute-Graph: A Graph Based Approach to Image Ranking
Prabhu, N., & Babu, R. V. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.128 · Cited by 27
The improbable but highly appropriate marriage of 3D stacking and neuromorphic accelerators
Belhadj, B., Valentian, A., Vivet, P., Duranton, M., He, L., & Temam, O. (2014) · International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES) · doi:10.1145/2656106.2656130 · Cited by 22
Learning deep features for multiple object tracking by using a multi-task learning strategy
Wang, L., Pham, N. T., Ng, T.-T., Wang, G., Chan, K. L., & Leman, K. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7025168 · Cited by 22
Filter-Invariant Image Classification on Social Media Photos
Chen, Y., Chao, T.-H., Bai, S.-Y., Lin, Y.-L., Chen, W., & Hsu, W. H. (2015) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2733373.2806348 · Cited by 20
Multimodal integration learning of object manipulation behaviors using deep neural networks
Noda, K., Arie, H., Suga, Y., & Ogata, T. (2013) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2013.6696582 · Cited by 20
Deep Search with Attribute-aware Deep Network
Huang, J., Xia, W., & Yan, S. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2654885 · Cited by 20
Visual co-occurrence network: using context for large-scale object recognition in retail
Advani, S., Smith, B., Tanabe, Y., Irick, K. M., Cotter, M. J., Sampson, J., & Narayanan, V. (2015) · IEEE/ACM/IFIP Workshop on Embedded Systems for Real-Time Multimedia (ESTIMedia) · doi:10.1109/estimedia.2015.7351774 · Cited by 18
Convolutional Neural Networks Learn Compact Local Image Descriptors
Osendorfer, C., Bayer, J., Urban, S., & van der Smagt, P. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-42051-1_77 · Cited by 20
Local receptive field constrained deep networks
Turcsány, D., Bargieła, A., & Maul, T. (2016) · Information Sciences · doi:10.1016/j.ins.2016.02.034 · Cited by 20
Representation Learning in Sensory Cortex: A Theory
Anselmi, F., & Poggio, T. (2022) · IEEE Access · doi:10.1109/access.2022.3208603 · Cited by 20
RFA at MediaEval 2015 Affective Impact of Movies Task: A Multimodal Approach
Mironică, I., Ionescu, B. E., Sjöberg, M., et al. (2015) · MediaEval · Cited by 20
Real-time personalized facial expression recognition system based on deep learning
Lee, I., Jung, H., Ahn, C. H., et al. (2016) · International Conference on Consumer Electronics · doi:10.1109/icce.2016.7430609 · Cited by 20
Increasing CAD system efficacy for lung texture analysis using a convolutional network
Tarando, S. R., Fetita, C. I., Faccinetto, A., & Brillet, P. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2217752 · Cited by 20
Zhang, H., Dana, K., & Nishino, K. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46493-0_49 · Cited by 25
Multiple deep features learning for object retrieval in surveillance videos
Guo, H., Wang, J., & Lu, H. (2016) · IET Computer Vision · doi:10.1049/iet-cvi.2015.0291 · Cited by 20
Convolutional Nonlinear Neighbourhood Components Analysis for Time Series Classification
Zheng, Y., Liu, Q., Chen, E., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-18032-8_42 · Cited by 20
Efficient Feature Coding Based on Auto-encoder Network for Image Classification
Xie, G.-S., Zhang, X.-Y., & Liu, C. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16865-4_41 · Cited by 19
Wang, Z., Lu, D., Zhang, D., Sun, M., & Zhou, Y. (2016) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-016-0429-9 · Cited by 19
Schaer, R., Müller, H., & Depeursinge, A. (2016) · Journal of Imaging · doi:10.3390/jimaging2020019 · Cited by 20
DeepSketch 2: Deep convolutional neural networks for partial sketch recognition
Seddati, O., Dupont, S., & Mahmoudi, S. (2016) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2016.7500261 · Cited by 20
An applet for the Gabor similarity scaling of the differences between complex stimuli
Margalit, E., Biederman, I., Herald, S. B., et al. (2016) · Attention Perception & Psychophysics · doi:10.3758/s13414-016-1191-7 · Cited by 20
Tattoo detection based on CNN and remarks on the NIST database
Xu, Q., Ghosh, S., Xu, X., Huang, Y., & Kong, A. W. (2016) · International Conference on Biometrics · doi:10.1109/icb.2016.7550050 · Cited by 19
Multimodal Deep Embedding via Hierarchical Grounded Compositional Semantics
Zhuang, Y., Song, J., Wu, F. Q., Li, X., Zhang, Z. M., & Rui, Y. (2016) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2016.2606648 · Cited by 20
Bridging the Semantic Gaps of GPU Acceleration for Scale-out CNN-based Big Data Processing
Song, M., Hu, Y., Xu, Y., Li, C., Chen, H., Yuan, J., & Li, T. (2016) · International Conference on Parallel Architectures and Compilation Techniques (PACT) · doi:10.1145/2967938.2967944 · Cited by 19
Category driven deep recurrent neural network for video summarization
Song, X., Chen, K., Lei, J., Sun, L., Wang, Z., Xie, L., & Song, M. (2016) · IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew.2016.7574720 · Cited by 20
A general purpose intelligent surveillance system for mobile devices using Deep Learning
Antoniou, A., & Angelov, P. P. (2016) · doi:10.1109/ijcnn.2016.7727563 · Cited by 20
Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP
Shim, Y., Philippides, A. O., Staras, K., & Husbands, P. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005137 · Cited by 21
Vehicle Logo Recognition and attributes prediction by multi-task learning with CNN
Xia, Y., Feng, J., & Zhang, B. (2016) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/fskd.2016.7603254 · Cited by 21
On the Exploration of Convolutional Fusion Networks for Visual Recognition
Liu, Y., Guo, Y., & Lew, M. S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-51811-4_23 · Cited by 20
A Dynamic Multi-precision Fixed-Point Data Quantization Strategy for Convolutional Neural Network
Shan, L., Zhang, M., Deng, L., & Gong, G. (2016) · Communications in computer and information science · doi:10.1007/978-981-10-3159-5_10 · Cited by 21
CNN-Based Object Segmentation in Urban LIDAR with Missing Points
Zelener, A., & Stamos, I. (2016) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.51 · Cited by 20
Roy, P. K., Chakravorty, R., Sedai, S., et al. (2016) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2016.7797012 · Cited by 20
Co-localization with Category-Consistent Features and Geodesic Distance Propagation
Le, H., Yu, C.-P., Zelinsky, G. J., & Samaras, D. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.134 · Cited by 21
Liu, Y., & Zheng, F. (2017) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2016.12.026 · Cited by 20
Multi-modal video event recognition based on association rules and decision fusion
Güder, M., & Çiçekli, N. K. (2017) · Multimedia Systems · doi:10.1007/s00530-017-0535-z · Cited by 20
Crowd Video Classification Using Convolutional Neural Networks
Burney, A., & Syed, T. (2016) · International Conference on Frontiers of Information Technology (FIT) · doi:10.1109/fit.2016.052 · Cited by 20
Zaki, H. F. M., Faisal Shafait, B., & Mian, A. S. (2017) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2017.02.008 · Cited by 20
Learning shape retrieval from different modalities
Tabia, H., & Laga, H. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.01.101 · Cited by 18
Color Enhancement With Adaptive Illumination Estimation for Low-Backlighted Displays
Pei, S., & Shen, C.-T. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2688924 · Cited by 20
bukhari, D., Wang, Y., & Wang, H. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.03.179 · Cited by 20
Gouveia, A., & Correia, M. P. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59153-7_38 · Cited by 23
Discriminatively Learned Hierarchical Rank Pooling Networks
Fernando, B., & Gould, S. J. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1030-x · Cited by 23
A Fast Feature Fusion Algorithm in Image Classification for Cyber Physical Systems
Wang, Y., Song, B., Zhang, P., Xin, N., & Cao, G. (2017) · IEEE Access · doi:10.1109/access.2017.2705798 · Cited by 22
Semantic web-mining and deep vision for lifelong object discovery
Young, J., Kunze, L., Basile, V., et al. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989323 · Cited by 20
Wang, X., Guo, Y., Wang, Y., & Yu, J.-H. (2017) · Neural computing & applications (Print) · doi:10.1007/s00521-017-3138-x · Cited by 18
Pham, T. D., Lee, D., & Park, K. R. (2017) · Sensors · doi:10.3390/s17071595 · Cited by 20
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
