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 32,601–32,700 of 108,435
MMT: Mixed-Mask Transformer for Remote Sensing Image Semantic Segmentation
Xu, Z., Geng, J., & Jiang, W. R. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3289408 · Cited by 25
ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot Learning
Rong, Y., Lu, X., Sun, Z., Chen, Y., & Xiong, S. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i8.26148 · Cited by 27
Resilient Binary Neural Network
Xu, S. Y., Li, Y., Ma, T., Lin, M., Dong, H., Zhang, B., Gao, P., & Lü, J. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i9.26261 · Cited by 26
In-Season Crop Type Detection by Combing Sentinel-1A and Sentinel-2 Imagery Based on the CNN Model
Mao, M., Zhao, H., Tang, G., & Ren, J. (2023) · Agronomy · doi:10.3390/agronomy13071723 · Cited by 27
Stasenko, N., Shukhratov, I., Savinov, M., Shadrin, D., & Somov, A. (2023) · Entropy · doi:10.3390/e25070987 · Cited by 26
Xing, D., Wang, Y., Sun, P., Huang, H., & Lin, E. (2023) · Plant Methods · doi:10.1186/s13007-023-01044-8 · Cited by 30
RGB-D Grasp Detection via Depth Guided Learning with Cross-modal Attention
Qin, R., Ma, H., Gao, B., & Huang, D. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161319 · Cited by 32
Date Fruit Classification Based on Surface Quality Using Convolutional Neural Network Models
Almomen, M., Al‐Saeed, M., & Ahmad, H. F. (2023) · Applied Sciences · doi:10.3390/app13137821 · Cited by 25
Ogunrinde, I. O., & Bernadin, S. (2023) · Sensors · doi:10.3390/s23146255 · Cited by 43
Zhang, M., Wen, G., Zhong, J., Chen, D., Wang, C., Huang, X., & Zhang, S. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3292312 · Cited by 25
Multi-view expressive graph neural networks for 3D CAD model classification
Li, S., & Corney, J. (2023) · Computers in Industry · doi:10.1016/j.compind.2023.103993 · Cited by 26
Enhancing Decentralized Federated Learning for Non-IID Data on Heterogeneous Devices
Chen, M., Xu, Y., Xu, H., & Huang, L. (2023) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde55515.2023.00177 · Cited by 27
MagConv: Mask-Guided Convolution for Image Inpainting
Yu, X., Xu, L., Li, J., & Ji, X. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3298536 · Cited by 25
Impact of Artificial Intelligence on Digital Marketing
Kar, S. (2023) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem25001 · Cited by 45
MASK-CNN-Transformer for real-time multi-label weather recognition
Chen, S., Shu, T., Zhao, H. Q., & Tang, Y. Y. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110881 · Cited by 26
Convolutional neural network framework for wind turbine electromechanical fault detection
Stone, E., Giani, S., Zappalá, D., & Crabtree, C. (2023) · Wind Energy · doi:10.1002/we.2857 · Cited by 24
Faroughi, S., Darvishi, A., & Rezaei, S. (2023) · Acta Mechanica · doi:10.1007/s00707-023-03676-2 · Cited by 27
Thiengburanathum, P., & Charoenkwan, P. (2022) · IEEE Access · doi:10.1109/access.2023.3308951 · Cited by 35
HyNet: A novel hybrid deep learning approach for efficient interior design texture retrieval
Chen, J., Shao, Z., Cen, C., & Li, J. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16579-0 · Cited by 25
Machine Vision and Image Analysis in Anesthesia: Narrative Review and Future Prospects
Lonsdale, H., Gray, G. M., Ahumada, L. M., & Matava, C. T. (2023) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000006679 · Cited by 25
Stratified random sampling for neural network test input selection
Wu, Z. J., Wang, Z., Chen, J., You, H., Yan, M., & Wang, L. (2023) · Information and Software Technology · doi:10.1016/j.infsof.2023.107331 · Cited by 30
Deep low-rank tensor embedding for multi-view subspace clustering
Liu, Z., & Song, P. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121518 · Cited by 28
Driver distraction detection based on lightweight networks and tiny object detection
Zhu, Z., Wang, S., Gu, S., Li, Y., Li, J., Shuai, L., & Qi, G. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023811 · Cited by 24
Guo, Z., Wen, J., & Xu, R. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3320658 · Cited by 26
A deep learning sequence model based on self-attention and convolution for wind power prediction
Liu, C., Chang, T.-Y., Yang, J.-S., & Huang, K.-B. (2023) · Renewable Energy · doi:10.1016/j.renene.2023.119399 · Cited by 28
Multi-Branch Enhanced Discriminative Network for Vehicle Re-Identification
Lian, J., Wang, D., Wu, Y. J., & Zhu, S. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3316068 · Cited by 25
SSD with multi-scale feature fusion and attention mechanism
Liu, Q., Dong, L., Zeng, Z., Zhu, W., Zhu, Y., & Chen, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-41373-1 · Cited by 28
Billion-Scale Bipartite Graph Embedding: A Global-Local Induced Approach
Wu, X., Xu, Y., Zhang, W., & Zhang, Y. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3626292.3626300 · Cited by 27
Ma, L., Khan, N. M., Feroze, T., et al. (2023) · Infrared Physics & Technology · doi:10.1016/j.infrared.2023.105084 · Cited by 25
Metasurfaces and their intelligent advances
Lv, J., Zhang, R., Gu, Q., Uddin, M. H., Jiang, X., Qi, J., Si, G., & Ou, Q. (2023) · Materials & Design · doi:10.1016/j.matdes.2023.112610 · Cited by 27
MD-UNet: a medical image segmentation network based on mixed depthwise convolution
Liu, Y., Yao, S., Wang, X., Chen, J., & Li, X. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-03005-8 · Cited by 27
Wang, Z., Peng, X., Zhou, H., Cao, S., Huang, W., Yan, W., Li, K., & Fan, S. (2024) · Energy · doi:10.1016/j.energy.2024.130270 · Cited by 25
Stable Cluster Discrimination for Deep Clustering
Qi, Q. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01526 · Cited by 25
DSSO-YOLO: A fast detection model for densely stacked small object
Zhang, Z., Liu, L., Zhao, X., Zhang, L., Wu, J., Zhang, Y., & Li, Z. (2024) · Displays · doi:10.1016/j.displa.2024.102659 · Cited by 26
Varejão, F. M., Mello, L. H. S., Ribeiro, M. P., Oliveira-Santos, T., & Rodrigues, A. L. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111452 · Cited by 30
Overview of temporal action detection based on deep learning
Hu, K., Shen, C., Wang, T., Xu, K., Xia, Q., Xia, M., & Cai, C. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10650-w · Cited by 32
YOLOv7-TS: A Traffic Sign Detection Model Based on Sub-Pixel Convolution and Feature Fusion
Zhao, S., Yuan, Y., Wu, X., Wang, Y., & Zhang, F. (2024) · Sensors · doi:10.3390/s24030989 · Cited by 29
Demir, F. B., Bayğın, M., Tuncer, I., Barua, P. D., Doğan, Ş., Tuncer, T., Ooi, C. P., Ciaccio, E. J., & Acharya, U. R. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18416-4 · Cited by 27
Lang, K., Cui, J., Yang, M., Wang, H., Wang, Z., & Shen, H. (2024) · Remote Sensing · doi:10.3390/rs16050906 · Cited by 30
Bhandari, B., Maung, P. T., Oromiehie, E., et al. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3377607 · Cited by 25
An explainable embedded neural system for on-board ship detection from optical satellite imagery
Ieracitano, C., Mammone, N., Spagnolo, F., et al. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108517 · Cited by 25
Jung, Y., Kong, J., Sheng, B., & Kim, J. (2024) · Journal of Computer Science and Technology · doi:10.1007/s11390-024-3419-7 · Cited by 27
A Review of Automatic Pain Assessment from Facial Information Using Machine Learning
Aoun, N. B. (2024) · Technologies · doi:10.3390/technologies12060092 · Cited by 23
Learning Vision from Models Rivals Learning Vision from Data
Tian, Y., Fan, L., Chen, K., Katabi, D., Krishnan, D., & Isola, P. (2024) · 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/cvpr52733.2024.01504 · Cited by 54
Neural shape codes for 3D model retrieval
Bai, S., Bai, X., Liu, W., & Roli, F. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.06.022 · Cited by 25
Big Data and the Internet of Things
Shah, M. (2015) · Studies in big data · doi:10.1007/978-3-319-26989-4_9 · Cited by 29
Adaptive region pooling for object detection
Tsai, Y., Hamsici, O. C., & Yang, M. (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.2015.7298673 · Cited by 27
Aodha, O. M., Stathopoulos, V., Brostow, G., Terry, M. A., Girolami, M., & Jones, K. E. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.12 · Cited by 25
Supervised deep learning with auxiliary networks
Zhang, J., Tian, G., Mu, Y., & Fan, W. (2014) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623618 · Cited by 24
Coninck, E. D., Verbelen, T., Vankeirsbilck, B., Bohez, S., Leroux, S., & Simoens, P. (2015) · doi:10.1145/2836127.2836130 · Cited by 27
Saliency Detection within a Deep Convolutional Architecture
Lin, Y., Kong, S., Wang, D., & Zhuang, Y. (2014) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · Cited by 25
Typical Target Detection in Satellite Images Based on Convolutional Neural Networks
Wu, H., Zhang, H., Zhang, J., & Xu, F. (2015) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2015.514 · Cited by 25
Pedestrian Classification and Detection in Far Infrared Images
Khellal, A., Ma, H., & Fei, Q. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-22879-2_47 · Cited by 25
Hybrid Algorithm for the Optimization of Training Convolutional Neural Network
Hayder, M., Han, T. X., & Naz, E. (2015) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2015.061011 · Cited by 26
Conjeti, S., Katouzian, A., Roy, A. G., Peter, L., Sheet, D., CARLIER, S. G., Laine, A. F., & Navab, N. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.02.005 · Cited by 24
An analysis of robust cost functions for CNN in computer-aided diagnosis
Barbu, A., Lü, L., Roth, H. R., Seff, A., & Summers, R. M. (2016) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2016.1138240 · Cited by 26
Texture classification for rail surface condition evaluation
Ma, K., Vicente, T. F. Y., Samaras, D., et al. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477597 · Cited by 25
Improving retail efficiency through sensing technologies: A survey
Quintana, M., Menéndez, J., Álvarez, F., & López, J. P. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.05.027 · Cited by 23
Multimodal learning for image popularity prediction on social media
Hu, J., Yamasaki, T., & Aizawa, K. (2016) · IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) · doi:10.1109/icce-tw.2016.7521017 · Cited by 25
Neuron Segmentation Based on CNN with Semi-Supervised Regularization
Xu, K., Su, H., Zhu, J., Guan, J., & Zhang, B. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.167 · Cited by 25
Temporal learning and sequence modeling for a job recommender system
Liu, K., Shi, X., Kumar, A., Zhu, L., & Natarajan, P. (2016) · ACM Recommender Systems Challenge (RecSys Challenge) · doi:10.1145/2987538.2987540 · Cited by 28
Valdenegro-Toro, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46182-3_18 · Cited by 27
Large Scale Data Enabled Evolution of Spoken Language Research and Applications
Jothilakshmi, S., & Gudivada, V. N. (2016) · Handbook of statistics · doi:10.1016/bs.host.2016.07.005 · Cited by 25
Song, Y., Cai, W., Huang, H., Feng, D. D., Wang, Y., & Chen, M. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1318-9 · Cited by 25
Building Correlations Between Filters in Convolutional Neural Networks
Wang, H., Chen, P., & Kwong, S. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2633552 · Cited by 25
Multi-view SAS image classification using deep learning
Williams, D. P., & Dugelay, S. (2016) · doi:10.1109/oceans.2016.7761334 · Cited by 25
Individual intelligent method-based fault diagnosis
Lei, Y. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-811534-3.00003-2 · Cited by 28
Chinese calligraphic style representation for recognition
Gao, P., Gang, G., Wu, J., & Baogang, W. (2017) · International Journal on Document Analysis and Recognition (IJDAR) · doi:10.1007/s10032-016-0277-z · Cited by 25
A Multi-purpose Image Counter-anti-forensic Method Using Convolutional Neural Networks
Yu, J., Zhan, Y., Yang, J., & Kang, X. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-53465-7_1 · Cited by 27
A high-efficiency runtime reconfigurable IP for CNN acceleration on a mid-range all-programmable SoC
Meloni, P., Deriu, G., Conti, F., Loi, I., Raffo, L., & Luca Benini, C. (2016) · International Conference on ReConFigurable Computing and FPGAs (ReConFig) · doi:10.1109/reconfig.2016.7857144 · Cited by 25
Convolutional Neural Network Architectures for the Automated Diagnosis of Celiac Disease
Wimmer, G., Hegenbart, S., Vécsei, A., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54057-3_10 · Cited by 25
CHAOS: a parallelization scheme for training convolutional neural networks on Intel Xeon Phi
Viebke, A., Memeti, S., Sabri Pllana, A., & Abraham, A. (2017) · The Journal of Supercomputing · doi:10.1007/s11227-017-1994-x · Cited by 25
Ren, X., Guo, H., He, G.-C., Xu, X., Di, C., & Li, S. (2016) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc.2016.18 · Cited by 29
Optic Disc Detection Using Fine Tuned Convolutional Neural Networks
Calimeri, F., Marzullo, A., Stamile, C., et al. (2016) · International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) · doi:10.1109/sitis.2016.20 · Cited by 25
Hybrid deep learning for Reflectance Confocal Microscopy skin images
Kaur, P., Dana, K., Cula, G. O., et al. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7899844 · Cited by 26
Potential Benefits of Combining Anomaly Detection with View Planning for UAV Infrastructure Modeling
Martin, R. E., Blackburn, L. D., Pulsipher, J. L., et al. (2017) · Remote Sensing · doi:10.3390/rs9050434 · Cited by 25
Shen, H., Pan, W. D., Dong, Y., & Alim, M. (2016) · Picture Coding Symposium · doi:10.1109/pcs.2016.7906393 · Cited by 28
Hajinoroozi, M., Mao, Z., Lin, Y., & Huang, Y. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-58628-1_4 · Cited by 24
Building extraction from high-resolution SAR imagery based on deep neural networks
Xu, Z., Wang, R., Zhang, H., et al. (2017) · Remote Sensing Letters · doi:10.1080/2150704x.2017.1335906 · Cited by 25
Convolutions through time for multi-label movie genre classification
Wehrmann, J., & Barros, R. C. (2017) · ACM Symposium on Applied Computing · doi:10.1145/3019612.3019641 · Cited by 25
An ensemble classification algorithm for convolutional neural network based on AdaBoost
Yang, S., Chen, L., Yan, T., Zhao, Y., & Fan, Y.-J. (2017) · IEEE/ACIS International Conference on Computer and Information Science (ICIS) · doi:10.1109/icis.2017.7960026 · Cited by 28
Li, X., Zhang, H., Zhang, X., Liu, H., & Xie, G. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037242 · Cited by 25
Scene-Adaptive Vehicle Detection Algorithm Based on a Composite Deep Structure
Cai, Y.-F., Wang, H., Zheng, Z., & Sun, X.-Q. (2017) · IEEE Access · doi:10.1109/access.2017.2756081 · Cited by 24
Deep Learning Model for Sentiment Analysis in Multi-lingual Corpus
Medrouk, L., & Pappa, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70087-8_22 · Cited by 27
Low Resolution Lidar-Based Multi-Object Tracking for Driving Applications
del Pino, I., Vaquero, V., Masini, B., et al. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-70833-1_24 · Cited by 25
Monocular depth estimation with guidance of surface normal map
Han, Y., Zhang, S., Zhang, Y., & Zhang, L. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.08.074 · Cited by 24
A GPU-based convolutional neural network approach for image classification
Cengil, E., Çınar, A., & Güler, Z. (2017) · International Artificial Intelligence and Data Processing Symposium (IDAP) · doi:10.1109/idap.2017.8090194 · Cited by 28
Convolutional Neural Network-Based Deep Urban Signatures with Application to Drone Localization
Amer, K., Samy, M., ElHakim, R., Shaker, M., & Elhelw, M. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.250 · Cited by 32
Blur image classification based on deep learning
Wang, R., Li, W., Qin, R., et al. (2017) · IEEE International Conference on Imaging Systems and Techniques (IST) · doi:10.1109/ist.2017.8261503 · Cited by 25
Large-scale Exploration of Neuronal Morphologies Using Deep Learning and Augmented Reality
Li, Z., Butler, E., Li, K., Lu, A., Ji, S., & Zhang, S. (2018) · Neuroinformatics · doi:10.1007/s12021-018-9361-5 · Cited by 25
End-to-End Scene Text Recognition in Videos Based on Multi Frame Tracking
Wang, X., Jiang, Y., Yang, S., Zhu, X., Li, W., Fu, P., Wang, H., & Luo, Z. (2017) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2017.207 · Cited by 26
A Computing Platform for Video Crowdprocessing Using Deep Learning
Lu, Z., Chan, K., & Porta, T. L. (2018) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2018.8486406 · Cited by 25
Li, H., Li, G., Ji, X., & Shi, L. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.02.019 · Cited by 24
Learning with Weak Supervision from Physics and Data‐Driven Constraints
Ren, H., Stewart, R., Song, J., et al. (2018) · AI Magazine · doi:10.1609/aimag.v39i1.2776 · Cited by 25
Machine Learning for Misfire Detection in a Dynamic Skip Fire Engine
Chen, S., Mandal, A., Chien, L.-C., et al. (2018) · SAE International Journal of Engines · doi:10.4271/2018-01-1158 · Cited by 25
Learning Image-Conditioned Dynamics Models for Control of Underactuated Legged Millirobots
Nagabandi, A., Yang, G., Asmar, T., Pandya, R., Kahn, G., Levine, S., & Fearing, R. S. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594193 · Cited by 34
Towards Accurate Handwritten Word Recognition for Hindi and Bangla
Dutta, K., Krishnan, P., Mathew, M., et al. (2018) · Communications in computer and information science · doi:10.1007/978-981-13-0020-2_41 · Cited by 25
Frontiers of Multimedia Research
Chang, S.-F. (2017) · doi:10.1145/3122865 · Cited by 26
Skarysz, A., Alkhalifah, Y., Darnley, K., Eddleston, M., Hu, Y., McLaren, D. B., Nailon, W. H., Salman, D., Sykora, M. D., Thomas, C. L. P., et al. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489539 · Cited by 25
Self-supervised autoencoders for clustering and classification
Nousi, P., & Tefas, A. (2018) · Evolving Systems · doi:10.1007/s12530-018-9235-y · Cited by 26
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
