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 34,701–34,800 of 108,435
Piran, F. J., Poduval, P., Barkam, H. E., Imani, M., & Imani, F. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110282 · Cited by 23
Hu, B., Shao, H., Shao, C., & Tang, W. (2025) · Solar Energy · doi:10.1016/j.solener.2025.113376 · Cited by 23
Edge and Flow Guided Iterative CNN for Remote Sensing Image Change Detection
Liu, Y., Li, S., He, Z., & Liu, K. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3550973 · Cited by 25
Huang, Q., Wu, S., Ou, Z., & Gao, Y. (2025) · Intelligent Oncology · doi:10.1016/j.intonc.2025.03.004 · Cited by 29
Hybrid of DSR-GAN and CNN for Alzheimer disease detection based on MRI images
Oraby, S., Emran, A. A., Saghir, B. M. E., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-94677-9 · Cited by 23
Biomedical Natural Language Processing in the Era of Large Language Models
Usuyama, N., Wong, C., Zhang, S., Naumann, T., & Poon, H. (2025) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-103123-095406 · Cited by 22
Jiang, C., Miao, K., Hu, Z., Gu, F., & Yi, K. (2025) · Processes · doi:10.3390/pr13051402 · Cited by 25
YOLOv8 framework for COVID-19 and pneumonia detection using synthetic image augmentation
Uddin, A. H., Abu, R. M., Yang, J., Bhatti, U. A., Ku, C. S., & Por, L. Y. (2025) · Digital Health · doi:10.1177/20552076251341092 · Cited by 25
Abdullahi, K., Ramakrishnan, K., & Ali, A. (2025) · Information · doi:10.3390/info16060451 · Cited by 33
Zhai, Q., Liu, Z., & Zhu, P. (2025) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2025.109129 · Cited by 22
Shang, Z., Zhao, S., Dang, Q., Wang, F., Sun, X., & Li, H. (2025) · ACS Catalysis · doi:10.1021/acscatal.5c02247 · Cited by 26
Zhou, X., & Hou, C. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106403 · Cited by 22
Autonomous robotic pruning in orchards and vineyards: A review
Navone, A., Martini, M., & Chiaberge, M. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.101283 · Cited by 35
Wang, R., Lin, H., Choi, J., Hashemi, A., & Zhu, M. (2025) · Energy · doi:10.1016/j.energy.2025.137651 · Cited by 23
Paddy-YOLO: An accurate method for rice pest detection
Zheng, Y., Zheng, W., & Du, X. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.110777 · Cited by 28
Deep learning in plant phenotyping: the first ten years
Ubbens, J. R., Stavness, I., Pound, M. P., & Guo, W. (2025) · Plant Phenomics · doi:10.1016/j.plaphe.2025.100062 · Cited by 29
“Just as progress appeared to slow, deep learning emerged as the new dominant technique in the computer vision literature [6].”
A Comparative Survey of Vision Transformers for Feature Extraction in Texture Analysis
Scabini, L. F. D. S., Sacilotti, A., Zielinski, K., Ribas, L. C., Bernard De Baets, C., & Bruno, O. M. (2025) · Journal of Imaging · doi:10.3390/jimaging11090304 · Cited by 44
Zhan, Z., Zhang, M., He, X., Li, X., Wang, Z., Chen, X., Han, B., Hu, W., Meng, Q., & Li, H. L. (2025) · International Journal of Structural Integrity · doi:10.1108/ijsi-12-2024-0209 · Cited by 29
Deep‑learning based osteoporosis classification in knee X‑rays using transfer‑learning approach
Qureshi, M. B., Sani, M., Raza, A., Qureshi, M. S., Alghamdi, W., Akbar, S., Kadir, R. A., & Sarker, M. R. (2025) · Scientific Reports · doi:10.1038/s41598-025-24338-4 · Cited by 27
Multi-scale memory network with separation training for hyperspectral anomaly detection
Huo, Y., Dong, Y., Wang, C., Zhang, M., & Wang, H. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104494 · Cited by 22
Rapid concerted switching of the neural code in the inferotemporal cortex
Shi, Y., Bi, D., Hesse, J. K., Lanfranchi, F. F., Chen, S., & Tsao, D. Y. (2026) · Nature · doi:10.1038/s41586-026-10267-3 · Cited by 35
Efficient monocular pose estimation for complex 3D models
Rubio, A., Villamizar, M., Ferraz, L., Peñate-Sánchez, A., Arnau Ramisa, A., Simo‐Serra, E., Sanfeliu, A., & Moreno-Noguer, F. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139372 · Cited by 22
Metric imitation by manifold transfer for efficient vision applications
Dai, D., Kroeger, T., Timofte, R., & Gool, L. V. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298975 · Cited by 20
Hueting, M., Ovsjanikov, M., & Mitra, N. J. (2015) · ACM Transactions on Graphics · doi:10.1145/2816795.2818097 · Cited by 20
Efficient Maximum Appearance Search for Large-Scale Object Detection
Chen, Q. K., Zheng, S., Feris, R. S., Datta, A., Cao, L., Huang, Z., & Yan, S. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.410 · Cited by 23
Energy-efficient vision on the PULP platform for ultra-low power parallel computing
Conti, F., Rossi, D., Pullini, A., Loi, I., & Benini, L. (2014) · SiPS ... design and implementation - IEEE Workshop on Signal Processing Systems · doi:10.1109/sips.2014.6986099 · Cited by 24
Topical Video Object Discovery from Key Frames by Modeling Word Co-occurrence Prior
Zhao, G., Yuan, J., & Hua, G. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.210 · Cited by 22
Convex Deep Learning via Normalized Kernels
Aslan, Ö., Zhang, X., & Schuurmans, D. (2014) · neural information processing systems · Cited by 23
Learning Deep Representation with Large-Scale Attributes
Ouyang, W., Li, H., Zeng, X., & Wang, X. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.220 · Cited by 21
Training Deformable Object Models for Human Detection Based on Alignment and Clustering
Drayer, B., & Brox, T. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10602-1_27 · Cited by 25
Compression of Deep Neural Networks on the Fly
Soulié, G., Gripon, V., & Robert, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-44781-0_19 · Cited by 23
Refining Architectures of Deep Convolutional Neural Networks
Shankar, S., Robertson, D., Ioannou, Y., Criminisi, A., & Cipolla, R. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.243 · Cited by 27
Zheng, Z., & Weng, J. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.9 · Cited by 21
Deep generic features and SVM for facial expression recognition
Vo, D. M., & Lê, T. H. (2016) · National Foundation for Science and Technology Development Conference on Information and Computer Science · doi:10.1109/nics.2016.7725672 · Cited by 22
Learning Text-Line Localization with Shared and Local Regression Neural Networks
Moysset, B., Louradour, J., Kermorvant, C., et al. (2016) · International Conference on Frontiers in Handwriting Recognition · doi:10.1109/icfhr.2016.0014 · Cited by 22
On line emotion detection using retrainable deep neural networks
Kollias, D., Tagaris, A., & Stafylopatis, A. (2016) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2016.7850049 · Cited by 22
Nonrecurrent Neural Structure for Long-Term Dependence
Zhang, S., Liu, C., Jiang, H., Wei, S., Dai, L.-R., & Hu, Y. (2017) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2017.2672398 · Cited by 85
Traffic light recognition using deep neural networks
Lee, G.-G., & Park, B. K. (2017) · International Conference on Consumer Electronics · doi:10.1109/icce.2017.7889317 · Cited by 22
MSR-CNN: Applying motion salient region based descriptors for action recognition
Tu, Z., Cao, J., Li, Y., & Li, B. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900180 · Cited by 22
Classroom Attendance Auto-management Based on Deep Learning
Wang, D., Fu, R., & Luo, Z. (2017) · International Conference on Education, Sports, Arts and Management Engineering (ICESAME) · doi:10.2991/icesame-17.2017.327 · Cited by 22
Batch-normalized Mlpconv-wise supervised pre-training network in network
Han, X., & Dai, Q. (2017) · Applied Intelligence · doi:10.1007/s10489-017-0968-2 · Cited by 24
Fusing Multilabel Deep Networks for Facial Action Unit Detection
Bishay, M., & Patras, I. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.86 · Cited by 21
A pipelined and scalable dataflow implementation of convolutional neural networks on FPGA
Bacis, M., Natale, G., Sozzo, E. D., & Santambrogio, M. D. (2017) · IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · doi:10.1109/ipdpsw.2017.44 · Cited by 23
Multi-Stream Deep Similarity Learning Networks for Visual Tracking
Li, K., Kong, Y., & Fu, Y. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/301 · Cited by 20
An energy-efficient and high-throughput bitwise CNN on sneak-path-free digital ReRAM crossbar
Ni, L., Liu, Z., Song, W., Yang, J. J., Yu, H., Wang, K., & Wang, Y. (2017) · International Symposium on Low Power Electronics and Design (ISLPED) · doi:10.1109/islped.2017.8009177 · Cited by 22
Embedding deep networks into visual explanations
Qi, Z., Khorram, S., & Li, F. (2020) · Artificial Intelligence · doi:10.1016/j.artint.2020.103435 · Cited by 26
QoE Multi-Stage Machine Learning for Dynamic Video Streaming
Grazia, M. D. F. D., Zucchetto, D., Testolin, A., Zanella, A., Zorzi, M., & Zorzi, M. (2017) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2017.2784449 · Cited by 22
Efficient fast convolution architectures for convolutional neural network
Xu, W., Wang, Z., You, X., & Zhang, C. (2017) · IEEE International Conference on ASIC (ASICON) · doi:10.1109/asicon.2017.8252623 · Cited by 22
Recognition of emotional speech with convolutional neural networks by means of spectral estimates
Weißkirchen, N., Böck, R., & Wendemuth, A. (2017) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw.2017.8272585 · Cited by 22
An analysis of Feature extraction and Classification Algorithms for Dangerous Object Detection
Kibria, S. B., & Hasan, M. S. (2017) · International Conference on Electrical & Electronic Engineering (ICEEE) · doi:10.1109/ceee.2017.8412846 · Cited by 22
DiverseNet: When One Right Answer is not Enough
Firman, M., Campbell, N. D. F., Agapito, L., et al. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00587 · Cited by 22
Exploring Energy and Accuracy Tradeoff in Structure Simplification of Trained Deep Neural Networks
Zhang, B., Davoodi, A., & Hu, Y. H. (2018) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2018.2833383 · Cited by 22
Object Recognition from very few Training Examples for Enhancing Bicycle Maps
Reinders, C., Ackermann, H., Yang, M. Y., et al. (2018) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2018.8500469 · Cited by 22
Cooking State Recognition from Images Using Inception Architecture
Salekin, M. S., Jelodar, A. B., & Kushol, R. (2019) · International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) · doi:10.1109/icrest.2019.8644262 · Cited by 22
Multi‐level image representation for large‐scale image‐based instance retrieval
Deng, Q., Wu, S., Wen, J., & Xu, Y. D. (2018) · CAAI Transactions on Intelligence Technology · doi:10.1049/trit.2018.0003 · Cited by 22
Evolving Convolutional Neural Network and Its Application in Fine-Grained Visual Categorization
Xuan, Q., Xiao, H., Fu, C., & Liu, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2842202 · Cited by 21
Salman, M., & Yüksel, S. E. (2018) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2018.8404199 · Cited by 22
VRSDNet: vehicle re-identification with a shortly and densely connected convolutional neural network
Zhu, J., Du, Y., Hu, Y., et al. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6270-4 · Cited by 22
Multiscale Fully Convolutional DenseNet for Semantic Segmentation
Brahimi, S., Aoun, N. B., Amar, C. B., Benoît, A., & Lambert, P. (2018) · Journal of WSCG · doi:10.24132/jwscg.2018.26.2.5 · Cited by 23
Balance gate controlled deep neural network
Liu, A., & Laili, Y. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.08.075 · Cited by 23
Image denoising via deep network based on edge enhancement
Chen, X., Zhan, S., Ji, D., Xu, L., Wu, C., & Li, X. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-1036-4 · Cited by 21
Human–Robot Collaborative Site Inspection Under Resource Constraints
Cai, H., & Mostofi, Y. (2018) · IEEE Transactions on Robotics · doi:10.1109/tro.2018.2875389 · Cited by 22
Multiview Semantic Representation for Visual Recognition
Zhang, C., Cheng, J., & Tian, Q. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2875728 · Cited by 22
Deep Reinforcement Learning Apply in Electromyography Data Classification
Song, C., Chen, C., Li, Y., & Wu, X. (2018) · IEEE International Conference on Cyborg and Bionic Systems (CBS) · doi:10.1109/cbs.2018.8612213 · Cited by 24
Modality Classification and Concept Detection in Medical Images Using Deep Transfer Learning
Singh, S., Ho‐Shon, K., Karimi, S., & Hamey, L. (2018) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1109/ivcnz.2018.8634803 · Cited by 22
RGB-D Object Recognition Using Multi-Modal Deep Neural Network and DS Evidence Theory
Zeng, H., Yang, B., Wang, X., Liu, J., & Fu, D. (2019) · Sensors · doi:10.3390/s19030529 · Cited by 22
Deep Learning–Based Multimedia Analytics
Zhang, W., Yao, T., Zhu, S., & Saddik, A. E. (2019) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3279952 · Cited by 26
Effective and Efficient Batch Normalization Using a Few Uncorrelated Data for Statistics Estimation
Chen, Z., Deng, L., Li, G., et al. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978753 · Cited by 22
ChaboNet : Design of a deep CNN for prediction of visual saliency in natural video
Chaabouni, S., Benois‐Pineau, J., & Amar, C. B. (2019) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2019.02.004 · Cited by 23
Transfer learning and U-Net for buildings segmentation
Adiba, A., Hajji, H., & Maâtouk, M. (2019) · doi:10.1145/3314074.3314088 · Cited by 22
Deep Learning for Plant Classification and Content-Based Image Retrieval
Gyires-Tóth, B., Osváth, M., Papp, D., et al. (2019) · Cybernetics and Information Technologies · doi:10.2478/cait-2019-0005 · Cited by 22
An Efficient Deep Convolutional Neural Network for Visual Image Classification
El-Rahiem, B. A., Ahmed, M., Reyad, O., El-Rahaman, H. A., Amin, M. A., & El-Samie, F. A. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-14118-9_3 · Cited by 23
TB-Places: A Data Set for Visual Place Recognition in Garden Environments
Leyva-Vallina, M., Strisciuglio, N., López-Antequera, M., Tyleček, R., Blaich, M., & Petkov, N. (2019) · IEEE Access · doi:10.1109/access.2019.2910150 · Cited by 22
Umapathy, A., Sreenivasan, A., Nairy, D. S., et al. (2019) · International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB) · doi:10.1145/3314367.3314376 · Cited by 22
Fedorchenko, I., Oliinyk, А., Stepanenko, А., Zaiko, T., Shylo, S., & Svyrydenko, A. (2019) · Eastern-European Journal of Enterprise Technologies · doi:10.15587/1729-4061.2019.164789 · Cited by 22
AUTOMATIC MUCK PILE CHARACTERIZATION FROM UAV IMAGES
Schenk, F., Tscharf, A., Mayer, G., & Fraundorfer, F. (2019) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iv-2-w5-163-2019 · Cited by 24
A Deep Convolutional Neural Network Model for Sense of Agency and Object Permanence in Robots
Lang, C., Schillaci, G., & Hafner, V. V. (2018) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/devlrn.2018.8761015 · Cited by 24
Pyramidal RoR for image classification
Zhang, K., Guo, L., Gao, C., & Zhao, Z. (2017) · Cluster Computing · doi:10.1007/s10586-017-1443-x · Cited by 21
A New Automatic Tool for CME Detection and Tracking with Machine-learning Techniques
Wang, P., Zhang, Y., Feng, L., Yuan, H., Gan, Y., Li, S., Lu, L., Ying, B., Gan, W., & Li, H. (2019) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ab340c · Cited by 27
Stochastic algorithms with geometric step decay converge linearly on sharp functions
Davis, D., Drusvyatskiy, D., & Charisopoulos, V. (2023) · Mathematical Programming · doi:10.1007/s10107-023-02003-w · Cited by 34
Personalized Pancreatic Tumor Growth Prediction via Group Learning
Zhang, L., Lü, L., Summers, R. M., Kebebew, E., & Yao, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66185-8_48 · Cited by 19
Synthetic Elastography Using B-Mode Ultrasound Through a Deep Fully Convolutional Neural Network
Wildeboer, R. R., van Sloun, R. J. G., Mannaerts, C. K., Moraes, P. H. D. M., Salomon, G., Chammas, M. C., Wijkstra, H., & Mischi, M. (2020) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2020.2983099 · Cited by 22
Moon, Y., Moon, H. S., Kim, D., Kim, J.-M., Lee, J.-K., Shim, W. H., Kim, S., Hwang, G., & Choi, J. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8091419 · Cited by 23
Machine Learning for Clinical Predictive Analytics
Weng, W. (2020) · Springer eBooks · doi:10.1007/978-3-030-47994-7_12 · Cited by 32
Learning Scene Attribute for Scene Recognition
Zeng, H., Song, X., Chen, G., et al. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2944241 · Cited by 22
Position-aware context attention for session-based recommendation
Cao, Y., Zhang, W., Song, B., Pan, W., & Xu, C. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.09.016 · Cited by 23
LGCN: Learnable Gabor Convolution Network for Human Gender Recognition in the Wild
Chen, P., Li, W., Sun, L., et al. (2019) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2018edl8239 · Cited by 22
InverSynth: Deep Estimation of Synthesizer Parameter Configurations From Audio Signals
Barkan, O., Tsiris, D., Katz, O., et al. (2019) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2019.2944568 · Cited by 22
Deep template matching for offline handwritten Chinese character recognition
Li, Z., Xiao, Y., Wu, Q., Jin, M., & Lu, H. (2019) · The Journal of Engineering · doi:10.1049/joe.2019.0895 · Cited by 23
Lindeberg, T. (2019) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-019-00915-x · Cited by 23
IIRNet: A lightweight deep neural network using intensely inverted residuals for image recognition
Li, Y., Zhang, D., & Lee, D.-J. (2019) · Image and Vision Computing · doi:10.1016/j.imavis.2019.10.005 · Cited by 22
Yang, Q., Xu, Z., Liao, C., Cai, J., Huang, Y., Chen, H., Tao, X., Huang, Z. W., Chen, J., Dong, J., et al. (2019) · Journal of Biophotonics · doi:10.1002/jbio.201900203 · Cited by 22
DNN pruning and mapping on NoC-Based communication infrastructure
Mirmahaleh, S. Y. H., & Rahmani, A. M. (2019) · Microelectronics Journal · doi:10.1016/j.mejo.2019.104655 · Cited by 23
Enhancing 2D Representation via Adjacent Views for 3D Shape Retrieval
Xu, C., Li, Z., Qiu, Q., Leng, B., & Jiang, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00383 · Cited by 21
Robust Embedded Deep K-means Clustering
Zhang, R., Tong, H., Xia, Y., et al. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3357985 · Cited by 22
Broccoli Seedling Segmentation Based on Support Vector Machine Combined With Color Texture Features
Zou, K., Ge, L., Zhang, C., Yuan, T., & Wei, L. (2019) · IEEE Access · doi:10.1109/access.2019.2954587 · Cited by 22
DeepLofargram: A deep learning based fluctuating dim frequency line detection and recovery
Han, Y., Li, Y., Liu, Q., & Ma, Y. (2020) · The Journal of the Acoustical Society of America · doi:10.1121/10.0002172 · Cited by 23
Robust Flexible Preserving Embedding
Lu, Y., Wong, W. K., Lai, Z., & Li, X. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2953922 · Cited by 21
Feature Consistency Training With JPEG Compressed Images
Wan, S., Wu, T.-Y., Hsu, H.-W., Wong, W. H., & Lee, C. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2959815 · Cited by 22
RecapNet: Action Proposal Generation Mimicking Human Cognitive Process
Wang, T., Chen, Y., Lin, Z., Zhu, A., Li, Y., Snoussi, H., & Wang, H. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2965196 · Cited by 21
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
