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 70,001–70,100 of 108,435
Automatic Fault Detection in Rails of Overhead Transport Systems for Semiconductor Fabs
Zhakov, A., Zhu, H., Siegel, A., et al. (2019) · Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) · doi:10.1109/asmc.2019.8791756 · Cited by 4
Reconfigurable Network for Efficient Inferencing in Autonomous Vehicles
Fang, S., & Choromanska, A. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794064 · Cited by 4
Exploring Deep Features and Transfer Learning for Plant Species Recognition
Feitoza, M. C., da Silva, W. B., & Calumby, R. T. (2019) · Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI) · doi:10.1145/3330204.3330264 · Cited by 5
Identification of Synthetic Activators of Cancer Cell Migration by Hybrid Deep Learning
Bruns, D., Gawehn, E., Kumar, K. S., Schneider, P., Baumgartner, M., & Schneider, G. (2019) · ChemBioChem · doi:10.1002/cbic.201900346 · Cited by 5
Deep Anchored Convolutional Neural Networks
Huang, J., Dwivedi, K., & Roig, G. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00089 · Cited by 4
Small-Scale Data Classification Based on Deep Forest
Zhang, M., & Zhang, Z. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29551-6_38 · Cited by 4
Second-Order Response Transform Attention Network for Image Classification
Zhang, J., Wang, J., Sun, Q., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2936446 · Cited by 4
Fang, Z., Liu, J., Liu, X., Tang, Q., Li, Y., & Lu, H. (2019) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3282469 · Cited by 4
Learning Bias-Free Representation for Large-Scale Person Re-Identification
Xu, J., & Zhu, E. (2019) · IEEE Access · doi:10.1109/access.2019.2937509 · Cited by 2
Headlight Range Estimation for Autonomous Driving using Deep Neural Networks
Mayr, J., Giracoglu, C., Unger, C., & Tombari, F. (2019) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2019.8813815 · Cited by 4
Bal, A., Banerjee, M., Sharma, P., & Chaki, R. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-8969-6_6 · Cited by 4
Sujee, R., & Sesh, V. B. (2019) · International Conference on Computer Communication and Informatics (ICCCI) · doi:10.1109/iccci.2019.8821874 · Cited by 4
Restoration as a Defense Against Adversarial Perturbations for Spam Image Detection
Jiang, J., Li, B., Yu, M., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30508-6_56 · Cited by 4
Spatial Attention Network for Few-Shot Learning
He, X., Qiao, P., Dou, Y., & Niu, X. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30484-3_46 · Cited by 4
Texture Classification Using Capsule Networks
Mamidibathula, B., Amirneni, S., Sistla, S. S., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-31332-6_51 · Cited by 4
An Exploration of the Interaction Between capsules with ResNetCaps models
Pucci, R., Micheloni, C., Roberto, V., Foresti, G. L., & Martinel, N. (2019) · International Conference on Distributed Smart Cameras · doi:10.1145/3349801.3349804 · Cited by 4
Harper, Z. J., & Welzig, C. M. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852300 · Cited by 3
Deep Learning Approaches Assessment for Underwater Scene Understanding and Egomotion Estimation
Teixeira, B., Silva, H. M., Matos, A. C., & Silva, E. (2019) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.23919/oceans40490.2019.8962872 · Cited by 5
Moriya, S., Yamamoto, H., Hirano‐Iwata, A., Kubota, S., & Sato, S. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852207 · Cited by 4
Scribbles for Metric Learning in Histological Image Segmentation
Harada, D., Bise, R., Tokunaga, H., Ohyama, W., Oka, S., Fujimori, T., & Uchida, S. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856465 · Cited by 4
Discriminative Features for Texture Retrieval Using Wavelet Packets
Vidal, A., Silva, J. F., & Busso, C. (2019) · IEEE Access · doi:10.1109/access.2019.2947006 · Cited by 4
Iterative machine learning based rotational alignment of brain 3D CT data
Chmelík, J., Jakubíček, R., Vičar, T., et al. (2019) · doi:10.1109/embc.2019.8857858 · Cited by 4
Ma, S., Wang, H., Shen, X., & Wang, X. (2019) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.1109/oceanse.2019.8867237 · Cited by 4
Recommendation System Based on Deep Learning
Gao, T., Jiang, L., & Wang, X. (2019) · Lecture notes in networks and systems · doi:10.1007/978-3-030-33506-9_48 · Cited by 4
shayegh, P., Li, Y., Zhang, J., et al. (2019) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1145/3350546.3352548 · Cited by 4
Bearing Fault Diagnosis Based on One-Dimensional Convolution Network and Residual Training
Li, B., Zhang, C., & Liu, G. W. (2019) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2019.8866426 · Cited by 4
Hailu, T. T., Yu, J., & Fantaye, T. G. (2019) · International Conference on Computer Science and Application Engineering (CSAE) · doi:10.1145/3331453.3361309 · Cited by 5
CNNs and Transfer Learning for Lecture Venue Occupancy and Student Attention Monitoring
Smith, A. J., van Wyk, B. J., & Du, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33723-0_31 · Cited by 6
Evaluation of Surrogate Models for Multi-fin Flapping Propulsion Systems
Viswanath, K., Sharma, A. J., Gabbita, S., et al. (2019) · OCEANS MTS/IEEE Conference (OCEANS) · doi:10.23919/oceans40490.2019.8962674 · Cited by 4
Gao, Z., Wang, L., Jojić, N., Niu, Z., Zheng, N., & Hua, G. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2866114 · Cited by 4
Understanding minority costumes: a computer vision perspective
Zhang, Q., Yang, Y., Yue, S., et al. (2019) · Multimedia Systems · doi:10.1007/s00530-019-00637-5 · Cited by 4
Empirical Analysis of Fixed Point Precision Quantization of CNNs
Ansari, A., & Ogunfunmi, T. (2019) · IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) · doi:10.1109/mwscas.2019.8885263 · Cited by 4
Multi-modal Feature Fusion Based on Variational Autoencoder for Visual Question Answering
Chen, L., Zhuo, Y., Wu, Y., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-31723-2_56 · Cited by 4
Zhang, W., Feng, W., Zhao, H., & Zhao, Q. (2019) · IEEE Access · doi:10.1109/access.2019.2952143 · Cited by 4
Neutrosophic set-based deep learning in mammogram analysis
Cai, G., Guo, Y., Chen, W., et al. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-818148-5.00014-x · Cited by 4
ARU-Net: Research and Application for Wrist Reference Bone Segmentation
Chen, L., Zhou, X., Wang, M., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2952608 · Cited by 4
Deeply Encoding Stable Patterns From Contaminated Data for Scenery Image Recognition
Zhang, L., Ju, X., Shang, Y., & Li, X. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2951798 · Cited by 5
MHEF-TripNet: Mixed Triplet Loss with Hard Example Feedback Network for Image Retrieval
Yang, X., Wan, S., Jin, P., Zou, C., & Li, X. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34113-8_4 · Cited by 4
Classification and Recognition of Medical Images Based on the SGTM Neuroparadigm.
Хавалко, В., Tsmots, I., Kostyniuk, A., & Strauß, C. (2019) · IDDM · Cited by 5
Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object Detection
Chen, P.-Y., Chang, M., Hsieh, J.-W., & Chen, Y. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3118953 · Cited by 142
LYRA: an Interactive and Interactive Storyteller
Choi, D. H. (2019) · IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) · doi:10.1109/cse/euc.2019.00037 · Cited by 5
A Web-Based Application for Identifying Objects In Images: Object Recognition Software
UCUZAL, H., CICEK, A. G. I. B., ARSLAN, A. G. A. K., & Çolak, C. (2019) · International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) · doi:10.1109/ismsit.2019.8932735 · Cited by 6
Improve the Accuracy of Neural Networks using Capsule Layers
Hollósi, J., & Pozna, C. R. (2018) · IEEE International Symposium on Computational Intelligence and Informatics (CINTI) · doi:10.1109/cinti.2018.8928194 · Cited by 4
Detecting Bone Fracture Using Transfer Learning
Verma, S. M., Kulshrestha, S., Rajput, C., et al. (2019) · Algorithms for intelligent systems · doi:10.1007/978-981-15-1100-4_10 · Cited by 4
Denoising The Wireless Channel Corrupted Images Using Machine Learning
Sufian, M., Khan, A., Saeed, T., & Khan, Z. (2019) · ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) · doi:10.1109/snpd.2019.8935803 · Cited by 4
View-Invariant and Similarity Learning for Robust Person Re-Identification
Ainam, J.-P., Qin, K., Liu, G., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2960030 · Cited by 4
Research on Recognition Method of Zanthoxylum Armatum Rust Based on Deep Learning
Xu, J., Wei, H., Ye, M., & Wang, W. (2019) · International Conference on Computational Biology and Bioinformatics (ICCBB) · doi:10.1145/3365966.3365975 · Cited by 4
Leveraging the Potency of CNN for Efficient Assessment of Visual Complexity of Images
Abdelwahab, M. A., Iliyasu, A. M., & Salama, A. (2019) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2019.8936095 · Cited by 4
Inter- and Intra-domain Knowledge Transfer for Related Tasks in Deep Character Recognition
Kooverjee, N., James, S. D., & van Zyl, T. L. (2020) · 2020 International SAUPEC/RobMech/PRASA Conference · doi:10.1109/saupec/robmech/prasa48453.2020.9041053 · Cited by 5
Automatic Degree Evaluation of Facial Nerve Paralysis Based on Triple-stream Long Short Term Memory
Yang, C., Kang, J., Xue, X., Zhou, Y., Wang, H., Wan, Z., Su, T., Xie, F., & Xu, P. (2019) · International Symposium on Image Computing and Digital Medicine (ISICDM) · doi:10.1145/3364836.3364838 · Cited by 5
Efficient Querying from Weighted Binary Codes
Weng, Z., & Zhu, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6919 · Cited by 4
Deep Neural Network Acceleration With Sparse Prediction Layers
Yao, Z., Huang, K., Shen, H., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2963941 · Cited by 4
An Automated Lung Nodule Segmentation Method Based On Nodule Detection Network and Region Growing
Tan, Y., Lü, K., & Xue, J. (2019) · ACM Multimedia Asia (MMAsia) · doi:10.1145/3338533.3366604 · Cited by 4
Mitra, V., & Franco, H. (2020) · Digital Signal Processing · doi:10.1016/j.dsp.2019.102655 · Cited by 3
Speech Recognition Model Compression
Sakthi, M., Tewfik, A. H., & Pawate, R. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053927 · Cited by 4
Traffic Speed Prediction for Urban Arterial Roads Using Deep Neural Networks
Adu-Gyamfi, Y. O., & Zhao, M. (2018) · ASCE International Conference on Transportation and Development (ICTD) · doi:10.1061/9780784481547.009 · Cited by 4
Deep Learning Approaches for Speech Emotion Recognition
Bhavan, A., Sharma, M., Piplani, M., Chauhan, P., Hitkul, & Shah, R. R. (2020) · Algorithms for intelligent systems · doi:10.1007/978-981-15-1216-2_10 · Cited by 4
WALLACE: Weakly Supervised Learning of Deep Convolutional Neural Networks With Multiscale Evidence
Liu, Y., Chen, W., Qu, H., Wang, T., Ji, J., & Miao, K. (2020) · IEEE Access · doi:10.1109/access.2020.2968545 · Cited by 4
Accuracy Improvement of Fashion Style Estimation with Attention Control of a Classifier
Aoki, R., Nakajima, T., Oki, T., et al. (2019) · International Conference on Consumer Electronics · doi:10.1109/icce-berlin47944.2019.8966159 · Cited by 4
Using Age Information as a Soft Biometric Trait for Face Image Analysis
Wang, H., Sánchez, V., Ouyang, W., & Li, C. (2020) · Unsupervised and semi-supervised learning · doi:10.1007/978-3-030-32583-1_1 · Cited by 4
Luca, M., Ciobanu, A., & Drug, V. L. (2019) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb47216.2019.8970041 · Cited by 4
The Rise of Data-Driven Models in Presentation Attack Detection
Pereira, L. A. M., Pinto, A. D. S., Andaló, F., Ferreira, A. M., Lavi, B., Soriano-Vargas, A., Cirne, M., & Rocha, A. D. R. (2020) · Unsupervised and semi-supervised learning · doi:10.1007/978-3-030-32583-1_13 · Cited by 3
Jiang, T. Y., & Wang, X. (2020) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-3-w10-335-2020 · Cited by 4
Real-Time Embedded Person Detection and Tracking for Shopping Behaviour Analysis
Schrijvers, R., Puttemans, S., Callemein, T., & Goedemé, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-40605-9_46 · Cited by 6
An image processing approach for rigid gas-permeable lens base-curve identification
Hashemi, S., Veisi, H., Jafarzadehpur, E., Rahmani, R., & Heshmati, Z. (2020) · Signal Image and Video Processing · doi:10.1007/s11760-019-01629-8 · Cited by 3
A Recommendation System of Sightseeing Places based on User's Behavior of Taking and Editing Photos
Shibamoto, E., Kittirojrattana, C., Koopipat, C., et al. (2019) · IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) · doi:10.1109/pacrim47961.2019.8985118 · Cited by 4
Yang, C., Zhang, B., Wei, H., et al. (2019) · IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) · doi:10.1109/icce-tw46550.2019.8991728 · Cited by 4
FMNet: Feature Mining Networks for Brain Tumor Segmentation
Lin, F., Liu, J., Wu, Q., et al. (2019) · Proceedings - International Conference on Tools with Artificial Intelligence, TAI · doi:10.1109/ictai.2019.00083 · Cited by 4
Salient Textural Anomaly Proposals and Classification for Metal Surface Anomalies
Natarajan, V., Mao, S., & Chia, L.-T. (2019) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2019.00092 · Cited by 4
R-CapsNet: An Improvement of Capsule Network for More Complex Data
Luo, L. F., Duan, S., & Wang, L. (2019) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci44817.2019.9003060 · Cited by 3
Image Generation and Style Transfer Using Conditional Generative Adversarial Networks
Murali, S., Rajati, M. R., & Suryadevara, S. (2019) · IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla.2019.00231 · Cited by 4
MobileGAN: Compact Network Architecture for Generative Adversarial Network
Shimizu, T., Xu, J., & Tasaka, K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-41404-7_23 · Cited by 4
Periocular Biometrics for Non-ideal Images Using Deep Convolutional Neural Networks
Kumari, P., & Seeja, K. R. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-1084-7_15 · Cited by 4
A Multilevel CNN Architecture for Character Recognition from Palm Leaf Images
Jyothi, R. L., & Rahiman, M. A. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-1084-7_19 · Cited by 4
Sharma, D. (2020) · Resonance · doi:10.1007/s12045-019-0919-9 · Cited by 4
An Improved Deep Mutual-Attention Learning Model for Person Re-Identification
Jamal, M. B., Zhengang, J., & Fang, M. (2020) · Symmetry · doi:10.3390/sym12030358 · Cited by 4
Simplistic Deep Learning for Japanese Handwritten Digit Recognition
Ghosh, A., Mukherjee, A., & Ghosh, C. (2020) · Learning and analytics in intelligent systems · doi:10.1007/978-3-030-42363-6_10 · Cited by 4
High-Throughput Deep Learning Microscopy Using Multi-Angle Super-Resolution
Zhang, J., Xu, T., Li, X., Zhang, Y., Chen, Y., Wang, X., Wang, S., & Wang, C. (2020) · IEEE photonics journal · doi:10.1109/jphot.2020.2977888 · Cited by 4
Shiloh, L., & Giryes, R. (2023) · Springer eBooks · doi:10.1007/978-3-031-24628-9_14 · Cited by 11
Hand Written Indian Numeral Character Recognition using Deep Learning approaches
Sivaanandh, M., Surya, S., & Priyanka, G. (2018) · International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE) · doi:10.1109/icrieece44171.2018.9008959 · Cited by 4
Learning Multifunctional Binary Codes for Personalized Image Retrieval
Liu, H., Wang, R., Shan, S., et al. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01315-0 · Cited by 4
On automated ear-based authentication
Kohlakala, A., & Coetzer, J. (2020) · 2020 International SAUPEC/RobMech/PRASA Conference · doi:10.1109/saupec/robmech/prasa48453.2020.9041089 · Cited by 4
An Effective Algorithm for Analysis and Processing of Satellite Images for Semantic Segmentation
Тюрин, А. А., Vorobiev, M. I., Lisov, O. I., et al. (2020) · IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering · doi:10.1109/eiconrus49466.2020.9039113 · Cited by 4
Bеrеzhnyi, A., Trystan, A., & Lavrov, O. (2020) · Advanced Information Systems · doi:10.20998/2522-9052.2020.1.01 · Cited by 4
Small-Sample Image Classification Method of Combining Prototype and Margin Learning
Li, X., Yu, L., Cao, J., Chang, D., Ma, Z., & Liu, N. (2019) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc47483.2019.9023037 · Cited by 5
Age and Gender Recognition Using Multi-task CNN
Vu, D.-Q., Phung, T. T. T., Wang, C.-Y., et al. (2019) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc47483.2019.9023045 · Cited by 4
Li, P., Zhang, D., Chen, P., Liu, X., & Wulamu, A. (2020) · IEEE Access · doi:10.1109/access.2020.2982034 · Cited by 4
Foreground Information Guidance for Siamese Visual Tracking
Li, D., & Yu, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2982261 · Cited by 4
Artificial Intelligence-Based Plant’s Diseases Classification
El-Maged, L. M. A., Darwish, A., & Hassanien, A. E. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-44289-7_1 · Cited by 4
Aurora Image Search With a Saliency-Weighted Region Network
Yang, X., Wang, N., Song, B., et al. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2952941 · Cited by 4
Anefficient Alternative to Network Pruning Through Ensemble Learning
Pollot, M., Zhang, R., & Kaup, A. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054583 · Cited by 4
Image Representation Method Based on Relative Layer Entropy for Insulator Recognition
Zhao, Z., Qi, H., Fan, X., et al. (2020) · Entropy · doi:10.3390/e22040419 · Cited by 4
A New Benchmark for Instance-Level Image Classification
Kang, K., Pang, G., Zhao, X., Wang, J., & Li, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2986771 · Cited by 4
A Comparative study for Feature Extraction and Classification of Images
Abdelmoneim, S. M., Kayed, M., & Taie, S. A. (2019) · International Conference on Advanced Control Circuits and Systems (ACCS) & International Conference on New Paradigms in Electronics & Information Technology (PEIT) · doi:10.1109/accs-peit48329.2019.9062848 · Cited by 5
A Novel Orthogonality Loss for Deep Hierarchical Multi-Task Learning
He, G., Huo, Y., He, M., Zhang, H., & Jianping Fan, B. (2020) · IEEE Access · doi:10.1109/access.2020.2985991 · Cited by 5
Smart Inference for Multidigit Convolutional Neural Network based Barcode Decoding
Do, T., Tolcha, Y., Jun, T. J., & Kim, D. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412707 · Cited by 7
Non-convex low-rank representation combined with rank-one matrix sum for subspace clustering
Liu, X., Wang, J., Cheng, D., et al. (2020) · Soft Computing · doi:10.1007/s00500-020-04865-0 · Cited by 4
Adaptive Hyper-Feature Fusion for Visual Tracking
Chen, Z., Du, Y., Deng, J., Zhuang, J., & Liu, P. (2020) · IEEE Access · doi:10.1109/access.2020.2986157 · Cited by 4
Deep Learning Based Language Identification System From Speech
Athira, N. P., & Poorna, S. S. (2019) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iccs45141.2019.9065370 · Cited by 4
Real-time Human Action Detection for Elderly Monitoring System
Gao, S., Wang, H., Xu, F., et al. (2019) · IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems · doi:10.1109/cyber46603.2019.9066751 · Cited by 4
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
