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 56,801–56,900 of 108,439
LACS: A High-Computational-Efficiency Accelerator for CNNs
Shang, J., Qian, L., Zhang, Z., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2962746 · Cited by 8
Vehicle Pollution Detection from Images Using Deep Learning
Kundu, S., & Maulik, U. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-2021-1_1 · Cited by 8
Majeed, Y., Karkee, M., Zhang, Q., Fu, L., & Whiting, M. D. (2019) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2019.12.501 · Cited by 9
A Risk Estimation Approach based on Deep Learning in Shipbuilding Industry
Choi, Y., Park, J.-H., & Jang, B. (2019) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc46691.2019.8939725 · Cited by 8
Development of Indonesian Speech Recognition with Deep Neural Network for Robotic Command
Anindya, C., Purwanto, D., & Ricoida, D. I. (2019) · International Seminar on Intelligent Technology and Its Applications · doi:10.1109/isitia.2019.8937275 · Cited by 8
Convolutional Approach Also Benefits Traditional Face Pattern Recognition Algorithm [208!]
Li, Y., Shi, H., Chen, L., & Jiang, F. (2019) · International Journal of Software Science and Computational Intelligence · doi:10.4018/ijssci.2019100101 · Cited by 8
A Driving Warning Method based on YOLOV3 and Neural Network
He, X., & Zheng, Z. (2019) · IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI) · doi:10.1109/soli48380.2019.8955050 · Cited by 8
3D shape recognition based on multi-modal information fusion
Liang, Q., Xiao, M., & Song, D. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08552-7 · Cited by 8
Camera Vignetting Model and its Effects on Deep Neural Networks for Object Detection
Saad, K., & Schneider, S.-A. (2019) · International Conference on Connected Vehicles and Expo · doi:10.1109/iccve45908.2019.8965233 · Cited by 9
A DenseNet feature-based loop closure method for visual SLAM system
Yu, C., Liu, Z., Liu, X.-J., Qiao, F., Wang, Y., Xie, F., Wei, Q., & Yang, Y. (2019) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio49542.2019.8961714 · Cited by 8
A study of conductance update method for Ni/SiNx/Si analog synaptic device
Kim, B., Choi, H.-S., & Kim, Y. (2020) · Solid-State Electronics · doi:10.1016/j.sse.2020.107772 · Cited by 8
Optimized feature space learning for generating efficient binary codes for image retrieval
Jose, A., Ottlik, E. S., Rohlfing, C., & Ohm, J.-R. (2021) · Signal Processing Image Communication · doi:10.1016/j.image.2021.116529 · Cited by 8
Representation learning in a deep network for license plate recognition
Rakhshani, S., Rashedi, E., & Nezamabadi–pour, H. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08416-0 · Cited by 8
CREMON: Cryptography Embedded on the Convolutional Neural Network Accelerator
Choi, Y., Sim, J., & Kim, L. (2020) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2020.2971580 · Cited by 8
Visual Terrain Classification Methods for Mobile Robots Using Hybrid Coding Architecture
Wu, H., Zhang, W., Li, B., et al. (2019) · IEEE International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc47709.2019.8981092 · Cited by 8
Zhang, W., Yan, Z., Wang, Q., Wu, X., & Zuo, W. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.02.001 · Cited by 8
Recovering Extremely Degraded Faces by Joint Super-Resolution and Facial Composite
Li, X., Duan, G., Wang, Z., et al. (2019) · Proceedings - International Conference on Tools with Artificial Intelligence, TAI · doi:10.1109/ictai.2019.00079 · Cited by 8
Hardware Acceleration of Multilayer Perceptron Based on Inter-Layer Optimization
Chen, S., & Lu, Z. (2019) · Proceedings, IEEE International Conference on Computer Design/Proceedings - IEEE International Conference on Computer Design · doi:10.1109/iccd46524.2019.00028 · Cited by 8
Bimodal Emotion Recognition Based on Audio and Facial Parts Using Deep Convolutional Neural Networks
Cornejo, J. Y. R., & Pedrini, H. (2019) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2019.00026 · Cited by 10
Gutierrez, K., & Santos, V. J. (2020) · IEEE Transactions on Haptics · doi:10.1109/toh.2020.2975555 · Cited by 8
Diverse receptive field network with context aggregation for fast object detection
Xie, S., Liu, C., Gao, J., Li, X., Luo, J., Fan, B., Chen, J., Pu, H., & Peng, Y. (2020) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2020.102770 · Cited by 8
Cigarette Detection Algorithm Based on Improved Faster R-CNN
Han, G., Li, Q., Zhou, Y., et al. (2019) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci44817.2019.9002702 · Cited by 8
Halcon-Based Solar Panel Crack Detection
Yao, G., & Wu, X. (2019) · World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM) · doi:10.1109/wcmeim48965.2019.00154 · Cited by 8
A Deep Learning Model for Extracting Live Streaming Video Highlights using Audience Messages
Han, H.-K., Huang, Y., & Chen, C. C. (2019) · Artificial Intelligence and Cloud Computing Conference (AICCC) · doi:10.1145/3375959.3375965 · Cited by 8
Priyasad, D., Fernando, T., Denman, S., Sridharan, S., & Fookes, C. (2019) · International Conference on Auditory-Visual Speech Processing (AVSP) · doi:10.21437/avsp.2019-5 · Cited by 10
Non-locally Enhanced Feature Fusion Network for Aircraft Recognition in Remote Sensing Images
Xiong, Y., Niu, X., Dou, Y., et al. (2020) · Remote Sensing · doi:10.3390/rs12040681 · Cited by 8
Multi-class Novelty Detection Using Mix-up Technique
Bhattacharjee, S., Mandal, D., & Biswas, S. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093303 · Cited by 10
From digital hype to analogue reality: Universal simulation beyond the quantum and exascale eras
Coveney, P. V., & Highfield, R. (2020) · Journal of Computational Science · doi:10.1016/j.jocs.2020.101093 · Cited by 9
Reference Grid-assisted Network for 3D Point Signature Learning from Point Clouds
Zhu, J., & Fang, Y. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093270 · Cited by 7
Inception DenseNet With Hybrid Activations For Image Classification
Zhang, J., & Feng, Z. (2019) · International Conference on Systems and Informatics (ICSAI) · doi:10.1109/icsai48974.2019.9010081 · Cited by 9
CSVM Architectures for Pixel-Wise Object Detection in High-Resolution Remote Sensing Images
Li, Y., Melgani, F., & He, B. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.2972289 · Cited by 8
Video-based Skeletal Feature Extraction for Hand Gesture Recognition
Lim, K. C., Sin, S. H., Lee, C., et al. (2020) · International Conference on Machine Learning and Soft Computing (ICMLSC) · doi:10.1145/3380688.3380711 · Cited by 8
Deep learning-based classification of multi-categorical Alzheimer's disease data.
Cohen, D. S., Carpenter, K. A., Jarrell, J. T., & Huang, X. (2019) · PubMed · Cited by 11
A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing
Firdaus, F., Ahmad, N. A., & Sahibuddin, S. (2020) · International journal of electrical and computer engineering systems · doi:10.32985/ijeces.10.2.2 · Cited by 9
A novel approach for deep pedestrian detection based on changes in camera viewing angle
Saeidi, M., & Ahmadi, A. (2020) · Signal Image and Video Processing · doi:10.1007/s11760-020-01662-y · Cited by 8
Application of Deep Learning to IMU sensor motion
Christian, M., Uyanik, C., Erdemir, E., et al. (2019) · Proceedings of IEEE Southeastcon · doi:10.1109/southeastcon42311.2019.9020363 · Cited by 8
Sirichotedumrong, W., Kinoshita, Y., & Kiya, H. (2019) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc47483.2019.9023091 · Cited by 10
Learning image representation from image reconstruction for a content-based medical image retrieval
Rohini, P., & Bindu, C. S. (2020) · Signal Image and Video Processing · doi:10.1007/s11760-020-01670-y · Cited by 8
Detecting Land Abandonment in Łódź Voivodeship Using Convolutional Neural Networks
Krysiak, S., Papińska, E., Majchrowska, A., et al. (2020) · Land · doi:10.3390/land9030082 · Cited by 8
A Channel-Blind Decoding for LDPC Based on Deep Learning and Dictionary Learning
Pang, X., Yang, C., Zhang, Z., et al. (2019) · IEEE International Workshop on Signal Processing Systems (SiPS) · doi:10.1109/sips47522.2019.9020628 · Cited by 8
Multi-scale Supervised Attentive Encoder-Decoder Network for Crowd Counting
Zhang, A., Jiang, X., Zhang, B., et al. (2020) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3356019 · Cited by 8
CTR Prediction Models Considering the Dynamics of User Interest
Zhang, H., Yan, J., & Zhang, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2988115 · Cited by 9
Vehicle Type Recognition based on Dimension Estimation and Bag of Word Classification
Dehkordi, R. A., & Khosravi, H. (2020) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.22044/jadm.2020.8375.1975 · Cited by 8
A Novel CNN Training Framework: Loss Transferring
Liang, C., Zhang, H., Yuan, D., & Zhang, M. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2989308 · Cited by 8
Classification of Chest Diseases Using Convolutional Neural Network
Ranjan, R., Singh, A. P., Rizvi, A., & Srivastava, T. (2020) · Lecture notes in networks and systems · doi:10.1007/978-981-15-3369-3_18 · Cited by 8
Intelligent painting identification based on image perception in multimedia enterprise
Wang, Y., Xu, Z., Bai, S., et al. (2020) · Enterprise Information Systems · doi:10.1080/17517575.2020.1755456 · Cited by 8
IKW: Inter-Kernel Weights for Power Efficient Edge Computing
Udupa, P., Mahale, G., Chandrasekharan, K. K., & Lee, S. (2020) · IEEE Access · doi:10.1109/access.2020.2993506 · Cited by 8
Learning Based Spectrum Hole Detection for Cognitive Radio Communication
Xu, Z., Petrunin, I. V., Tsourdos, A., & Ayub, S. (2019) · IEEE/AIAA Digital Avionics Systems Conference (DASC) · doi:10.1109/dasc43569.2019.9081799 · Cited by 9
Automated Multiclass Diagnosis of Diabetic Retinopathy using Hierarchical Learning
Shrivastava, U., & Joshi, M. V. (2018) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/3293353.3293412 · Cited by 9
Wang, Y., & Cao, Y. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02180-2 · Cited by 10
Research on Intelligent Vehicle Damage Assessment System Based on Computer Vision
Qianqian, Z., Guo, W., Ying, S., & Zhao, Z. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1518/1/012050 · Cited by 8
Two-Stage Spatial Mapping for Multimodal Data Fusion in Mobile Crowd Sensing
Zhou, J., Xu, T., Ren, S., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2995268 · Cited by 8
Monocular depth estimation with SPN loss
Mathew, A., & Mathew, J. (2020) · Image and Vision Computing · doi:10.1016/j.imavis.2020.103934 · Cited by 7
Multimodal Biometrics Data Analysis for Gender Estimation Using Deep Learning
Gornale, S. S., Patil, A., & Ramchandra, K. (2020) · International Journal of Data Science and Analysis · doi:10.11648/j.ijdsa.20200602.11 · Cited by 8
Multivariate Time Series Classification Based on MCNN-LSTMs Network
Guo, Z., Liu, P., Yang, J., et al. (2020) · International Conference on Machine Learning and Computing (ICMLC) · doi:10.1145/3383972.3384013 · Cited by 8
Image Restoration via Deep Memory-Based Latent Attention Network
Zhang, X., Gao, P., Zhao, K., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2999965 · Cited by 8
Real-time detection of UAV detection image of power line insulator bursting based on YOLOV3
Chen, S., Su, C., Kuang, Z., et al. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1544/1/012117 · Cited by 8
Two Decades of AI4NETS - AI/ML for Data Networks: Challenges & Research Directions
Casas, P. (2020) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms47738.2020.9110266 · Cited by 12
Consistent Domain Structure Learning and Domain Alignment for 2D Image-Based 3D Objects Retrieval
Su, Y., Li, Y., Song, D., Nie, W., Li, W., & Liu, A.-A. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/123 · Cited by 8
Cross-modal video moment retrieval based on visual-textual relationship alignment
Xu, T. B., Du, H. Y., Chen, E., Chen, J., & Yufei, W. U. (2020) · Scientia Sinica Informationis · doi:10.1360/ssi-2019-0292 · Cited by 8
Is the Skip Connection Provable to Reform the Neural Network Loss Landscape?
Wang, L., Shen, B., Zhao, N., & Zhang, Z. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/387 · Cited by 10
ABC: Abstract prediction Before Concreteness
Kim, J.-E., Bradford, R. M., Yoon, M.-K., & Shao, Z. (2020) · Design, Automation, and Test in Europe · doi:10.23919/date48585.2020.9116479 · Cited by 8
Calandra, H., Gratton, S., Riccietti, E., et al. (2020) · Optimization methods & software · doi:10.1080/10556788.2020.1775828 · Cited by 8
Deep Convolutional Neural Network for Detection of Solar Panels
Golovko, V. A., Kroshchanka, A., Mikhno, E., Komar, M., & Sachenko, A. (2020) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-43070-2_17 · Cited by 10
From Local to Global: A Graph Framework for Retinal Artery/Vein Classification
Huang, F., Tan, T., Dashtbozorg, B., Zhou, Y., & Romeny, B. M. T. H. (2020) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2020.3004481 · Cited by 9
Pham, V. V., Nguyen, N., & Dang, T. (2020) · doi:10.1145/3406601.3406644 · Cited by 7
Discovering and incorporating latent target-domains for domain adaptation
Li, H., Li, W., & Wang, S. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107536 · Cited by 8
Segment Medical Image Using U-Net Combining Recurrent Residuals and Attention
Wang, Y., He, Z., Xie, P., Yang, C., Zhang, Y., Li, F., Chen, X., Lü, K., Li, T., Zhou, J., et al. (2020) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-5199-4_8 · Cited by 10
Samragh, M., Javaheripi, M., & Koushanfar, F. (2020) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3391901 · Cited by 10
Generative Latent Implicit Conditional Optimization when Learning from Small Sample
Azuri, I., & Weinshall, D. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9413259 · Cited by 8
Aldwairi, T., Perera, D., & Novotny, M. A. (2020) · Electronics · doi:10.3390/electronics9071167 · Cited by 10
Deep dilated and densely connected parallel convolutional groups for compression artifacts reduction
Amaranageswarao, G., Deivalakshmi, S., & Ko, S. (2020) · Digital Signal Processing · doi:10.1016/j.dsp.2020.102804 · Cited by 8
Kwak, M., Lee, S., Kim, S., & Hwang, H. (2020) · IEEE Electron Device Letters · doi:10.1109/led.2020.3008936 · Cited by 8
Residual Forward-Subtracted U-Shaped Network for Dynamic and Static Image Restoration
Jung, H. M., Kim, B. H., & Kim, M. Y. (2020) · IEEE Access · doi:10.1109/access.2020.3011580 · Cited by 8
IRON-MAN: An Approach to Perform Temporal Motionless Analysis of Video Using CNN in MPSoC
Dey, S., Singh, A. K., Prasad, D. K., & McDonald-Maier, K. D. (2020) · IEEE Access · doi:10.1109/access.2020.3010185 · Cited by 7
ADABench - Towards an Industry Standard Benchmark for Advanced Analytics
Rabl, T., Brücke, C., Härtling, P., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-55024-0_4 · Cited by 8
Božić-Štulić, D., Braović, M., & Stipaničev, D. (2020) · International journal of electrical and computer engineering systems · doi:10.32985/ijeces.11.2.6 · Cited by 8
Interpretation of artificial intelligence studies for the ophthalmologist
Tan, T., Xu, X., Wang, Z., Liu, Y., & Ting, D. S. W. (2020) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000000695 · Cited by 8
Deep Learning for Sentiment Analysis
Karas, V., & Schuller, B. W. (2020) · Advances in business information systems and analytics book series · doi:10.4018/978-1-7998-4240-8.ch005 · Cited by 8
Fragmented handwritten digit recognition using grading scheme and fuzzy rules
Chaki, J., & Dey, N. (2020) · Sadhana · doi:10.1007/s12046-020-01410-5 · Cited by 8
Data Generation Using Gene Expression Generator
Farou, Z., Mouhoub, N., & Horváth, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-62365-4_6 · Cited by 8
TrustServing: A Quality Inspection Sampling Approach for Remote DNN Services
Hou, X., & Han, T. (2020) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/secon48991.2020.9158444 · Cited by 8
The Role of Collative Variables in Aesthetic Experiences
Marin, M. M. (2020) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198824350.013.20 · Cited by 11
Signal Frequency Estimation Based on RNN
Huang, B., Lin, C., Chen, W., Juang, C., & Wu, X. (2020) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc49329.2020.9164504 · Cited by 7
Single Image Super-resolution Reconstruction with Wavelet based Deep Residual Learning
Dou, J., Tu, Z., & Peng, X. (2020) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc49329.2020.9164678 · Cited by 8
An Improved Combination of Image Denoisers Using Spatial Local Fusion Strategy
Liu, Y., Xu, S., & Lin, Z. (2020) · IEEE Access · doi:10.1109/access.2020.3016766 · Cited by 8
Thermal Infrared Tracking using Multi-stages Deep Features Fusion
Zhang, X., Chen, R., Liu, G., et al. (2020) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc49329.2020.9164750 · Cited by 8
Urban 3D Structure Reconstruction Through a Generative Adversarial Network Model
Aithal, B. H., Das, S. K., & Prakash, P. S. (2020) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-020-04850-7 · Cited by 8
DA-Net: Pedestrian Detection Using Dense Connected Block and Attention Modules
Yin, R., Zhang, R., Zhao, W., & Jiang, F. (2020) · IEEE Access · doi:10.1109/access.2020.3018306 · Cited by 9
Concealed Threat Detection based on Multi-view Millimeter Wave Imaging for Human Body
Jiang, Y., Cui, J., Chen, Z., et al. (2019) · IEEE International Conference on Signal, Information and Data Processing (ICSIDP) · doi:10.1109/icsidp47821.2019.9173419 · Cited by 8
Music Information Retrieval and Contemporary Classical Music: A Successful Failure
Cella, C.-E. (2020) · Transactions of the International Society for Music Information Retrieval · doi:10.5334/tismir.55 · Cited by 9
Adversarially Robust Learning via Entropic Regularization
Jagatap, G., Joshi, A., Chowdhury, A. B., Garg, S., & Hegde, C. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.780843 · Cited by 13
Satellite Image Classification with Data Augmentation and Convolutional Neural Network
Dave, P. R., & Pandya, H. A. (2020) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-5558-9_9 · Cited by 7
Video Analysis System Using Deep Learning Algorithms
Hernández, G., Rodríguez, S., Arrieta, A. G., Corchado, J. M., & Prieto, J. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-58356-9_19 · Cited by 8
Unique Faces Recognition in Videos
Huo, J., & van Zyl, T. L. (2020) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion45008.2020.9190469 · Cited by 8
Efficient fall activity recognition by combining shape and motion features
Iazzi, A., Rziza, M., & Thami, R. O. H. (2020) · Computational Visual Media · doi:10.1007/s41095-020-0183-7 · Cited by 8
Dual Channel Attention Networks
Bian, J., & Liu, Y. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1642/1/012004 · Cited by 8
A Lightweight CNN Architecture for Land Classification on Satellite Images
Patowary, G., Agarwalla, M., Agarwal, S., et al. (2020) · International Conference on Computational Performance Evaluation (ComPE) · doi:10.1109/compe49325.2020.9200100 · Cited by 8
Logo detection using weakly supervised saliency map
Kumar, G., Keserwani, P., Roy, P. P., & Dogra, D. P. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09813-6 · Cited by 8
Automated Object Labeling For Cnn-Based Image Segmentation
Novozámský, A., Vít, D., Šroubek, F., et al. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9191320 · Cited by 8
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
