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 54,701–54,800 of 108,439
Das, D., Mahanta, L. B., Baishya, B. K., & Ahmed, S. (2020) · International Conference on Computer, Electrical & Communication Engineering (ICCECE) · doi:10.1109/iccece48148.2020.9223104 · Cited by 13
Click-Through Rate Prediction Combining Mutual Information Feature Weighting and Feature Interaction
Wang, X., Dong, H., & Han, S. (2020) · IEEE Access · doi:10.1109/access.2020.3034630 · Cited by 9
Deep InterBoost networks for small-sample image classification
Li, X., Chang, D., Ma, Z., Tan, Z., Xue, J., Cao, J., & Guo, J. H. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.06.135 · Cited by 9
Dual-View Semantic Inference Network for image-text matching
Wu, C., Wu, J., Cao, H., Wei, Y., & Wang, L. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.09.079 · Cited by 9
A Non-Array Type Cut to Shape Soft Slip Detection Sensor Applicable to Arbitrary Surface
Kim, S. J., Lee, S. H., Moon, H., Choi, H. R., & Koo, J. C. (2020) · Sensors · doi:10.3390/s20216185 · Cited by 10
A Novel, Efficient Implementation of a Local Binary Convolutional Neural Network
Lin, I.-C., Tang, C.-H., Ni, C.-T., Hu, X., Shen, Y., Chen, P., & Xie, Y. (2020) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2020.3036012 · Cited by 10
Parameswaran, S. K., & Chinnusamy, G. (2020) · Sadhana · doi:10.1007/s12046-020-01500-4 · Cited by 9
Attentive Feedback Feature Pyramid Network for Shadow Detection
Kim, J.-H., & Kim, W. (2020) · IEEE Signal Processing Letters · doi:10.1109/lsp.2020.3034527 · Cited by 9
Sensors-based Human Activity Recognition with Convolutional Neural Network and Attention Mechanism
Zhang, W., Zhu, T., Yang, C., Xiao, J., & Ning, H. (2020) · IEEE International Conference on Software Engineering and Service Science (ICSESS) · doi:10.1109/icsess49938.2020.9237720 · Cited by 12
Explainable Deep Learning Methods for Medical Imaging Applications
Vuppala, S. K., Behera, M., Jack, H., & Bussa, N. (2020) · International Conference on Computing, Communication and Automation · doi:10.1109/iccca49541.2020.9250820 · Cited by 13
A Hierarchical K-Means-Assisted Scenario-Aware Reconfigurable Convolutional Neural Network
Chen, K.-C. J., Huang, Y.-W., Liu, G.-M., Liang, J., Yang, Y.-C., & Liao, Y.-H. (2020) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2020.3034351 · Cited by 9
High-Order Triplet CRF-PCANet for Unsupervised Segmentation of Nonstationary SAR Image
Zhang, P., Boudaren, M. E. Y., Jiang, Y., et al. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3035665 · Cited by 9
Improving the Feature Set in IoT Intrusion Detection Problem Based on FP-Growth Algorithm
Van, L. T. H., Hưởng, P. V., Thuận, L. Đ., & Minh, N. H. (2020) · International Conference on Advanced Technologies for Communications · doi:10.1109/atc50776.2020.9255431 · Cited by 9
Takizawa, A., & Kinugawa, H. (2020) · Design Science · doi:10.1017/dsj.2020.27 · Cited by 9
Directional Neighborhood Topologies Based Multi-Scale Quinary Pattern for Texture Classification
Rachdi, E., Merabet, Y. E., Akhtar, Z., & Messoussi, R. (2020) · IEEE Access · doi:10.1109/access.2020.3040136 · Cited by 9
Human-Computer Synergies in Prosthetic Interactions
Carroll, J. M., Lee, S., Reddie, M., Beck, J., & Rosson, M. B. (2020) · Interaction design & architecture(s)/ID&A Interaction design & architecture(s) · doi:10.55612/s-5002-044-002 · Cited by 9
Rabaev, I., Barakat, B. K., Churkin, A., & El‐Sana, J. (2020) · International Conference on Frontiers in Handwriting Recognition · doi:10.1109/icfhr2020.2020.00050 · Cited by 9
Periodic Time Series Data Analysis by Deep Learning Methodology
Zhang, H., Lu, H., & Nayak, A. R. (2020) · IEEE Access · doi:10.1109/access.2020.3041600 · Cited by 9
You, Y., Lysenko, A. V., Qiu, J., et al. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105889 · Cited by 9
Transfer-Deep Learning Application for Ultrasonic Computed Tomographic Image Classification
Fradi, M., Afif, M., Zahzeh, E.-H., Bouallegue, K., & Machhout, M. (2020) · International Conference on Control, Automation and Diagnosis (ICCAD) · doi:10.1109/iccad49821.2020.9260569 · Cited by 9
Radiography Classification: A Comparison between Eleven Convolutional Neural Networks
Ananda, A., Karabağ, C., Ter-Sarkisov, A., Alonso, E., & Reyes‐Aldasoro, C. C. (2020) · International Conference on Multimedia Computing, Networking and Applications (MCNA) · doi:10.1109/mcna50957.2020.9264285 · Cited by 10
Multi-view Adaptive Graph Convolutions for Graph Classification
Adaloglou, N., Vretos, N., & Daras, P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58574-7_24 · Cited by 11
Kapoor, N., Bär, A., Varghese, S., et al. (2021) · doi:10.1109/ijcnn52387.2021.9534145 · Cited by 9
Few-shot learning with saliency maps as additional visual information
Abdelaziz, M., & Zhang, Z. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09875-6 · Cited by 9
Herráiz, Á. H., Martínez‐Rodrigo, A., Puchol, A., et al. (2020) · International Conference on e-Health and Bioengineering (EHB) · doi:10.1109/ehb50910.2020.9280217 · Cited by 9
Wang, X., Chen, Y., Ruan, W., Gao, Q., Guode, Y., & Dong, L. M. (2020) · Scientific Programming · doi:10.1155/2020/8828745 · Cited by 9
Rakhshani, H., Idoumghar, L., Ghambari, S., Lepagnot, J., & Brévilliers, M. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107596 · Cited by 11
Class label autoencoder with structure refinement for zero-shot learning
Lin, G., Fan, C., Chen, W., Chen, Y., & Zhao, F. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.11.061 · Cited by 12
Posthurricane damage assessment using satellite imagery and geolocation features
Cao, Q. D., & Choe, Y. (2023) · Risk Analysis · doi:10.1111/risa.14244 · Cited by 14
Image-Based River Water Level Estimation for Redundancy Information Using Deep Neural Network
Fleury, G. R. D. O., do Nascimento, D. V., Filho, A. R. G., Ribeiro, F. D. S. L., de Carvalho, R. V., & Coelho, C. J. (2020) · Energies · doi:10.3390/en13246706 · Cited by 9
Zhu, M. S., Zhao, Z., Li, B., Su, F., Guo, L., & Wang, H. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3043589 · Cited by 10
CnnSound: Convolutional Neural Networks for the Classification of Environmental Sounds
Şeker, H., & İni̇k, Ö. (2020) · doi:10.1145/3441417.3441431 · Cited by 9
Image Aesthetic Assessment Based on Image Classification and Region Segmentation
Le, Q.-T., Ladret, P., Nguyen, H.-T., & Caplier, A. (2020) · Journal of Imaging · doi:10.3390/jimaging7010003 · Cited by 9
ResCovNet: A Deep Learning-Based Architecture For COVID-19 Detection From Chest CT Scan Images
Dastider, A. G., Subah, M. R., Sadik, F., Mahmud, T., & Fattah, S. A. (2020) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon50793.2020.9293887 · Cited by 9
Training deep retrieval models with noisy datasets: Bag exponential loss
Martínez-Cortés, T., González-Díaz, I., & Díaz-de-María, F. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107811 · Cited by 9
Schubert, S., Neubert, P., & Protzel, P. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3052446 · Cited by 10
Fast vertex-based graph convolutional neural network and its application to brain images
Liu, C., Ji, H., & Qiu, A. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.12.097 · Cited by 9
Kang, J. K., Lee, M. B., Yoon, H. S., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3051637 · Cited by 9
Localization-Aware Adaptive Pairwise Margin Loss for Fine-Grained Image Recognition
Kim, T., Kim, H., & Byun, H. (2021) · IEEE Access · doi:10.1109/access.2021.3049305 · Cited by 8
Neuroevolution Architecture Backbone for X-ray Object Detection
Operiano, K. R. G., Iba, H., & Pora, W. (2020) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci47803.2020.9308453 · Cited by 10
Pellicer-Valero, Ó. J., González‐Pérez, V., Ramón-Borja, J. L. C., García, I. M., Benito, M. B., Gómez, P. P., Rubio‐Briones, J., Rupérez, M. J., & Martín‐Guerrero, J. D. (2021) · Applied Sciences · doi:10.3390/app11020844 · Cited by 9
Comparison of Machine Learning Methods in Stochastic Skin Optical Model Inversion
Annala, L., Äyrämö, S., & Pölönen, I. (2020) · Applied Sciences · doi:10.3390/app10207097 · Cited by 9
Zhong, Y., Bi, T.-Q., Wang, J., Wu, S., Jiang, T., & Huang, Y. (2021) · Ad hoc networks · doi:10.1016/j.adhoc.2021.102438 · Cited by 5
Mechanical Fault Diagnosis Methods Based on Convolutional Neural Network: a Review
Zhang, T., & Dai, J. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1750/1/012048 · Cited by 9
Domain adaptation with feature and label adversarial networks
Zhao, P., Zang, W., 刘, 斌., Kang, Z., Bai, K., Huang, K., & Xu, Z. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.01.062 · Cited by 9
Method for Generating Captions for Clothing Images to Support Visually Impaired People
Tateno, K., Takagi, N., Sawai, K., Masuta, H., & Motoyoshi, T. (2020) · Joint International Conference on Soft Computing and Intelligent Systems and International Symposium on Advanced Intelligent Systems (SCIS&ISIS) · doi:10.1109/scisisis50064.2020.9322767 · Cited by 9
Visualizing One Pixel Attack Using Adversarial Maps
Wang, W., Sun, J. Q., & Wang, G. (2020) · Chinese Automation Congress · doi:10.1109/cac51589.2020.9327603 · Cited by 8
A Deep Learning Framework for Solving Rectangular Waveguide Problems
Hu, X., & Buris, N. E. (2020) · IEEE Asia-Pacific Microwave Conference (APMC) · doi:10.1109/apmc47863.2020.9331654 · Cited by 9
Deep Neural Networks with Multi-class SVM for Recognition of Cross-Spectral Iris Images
Sandhya, M., Rudani, U., Vallabhadas, D. K., et al. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-0419-5_3 · Cited by 9
Iterative Convolutional Neural Network-Based Illumination Estimation
Koščević, K., Subašić, M., & Lončarić, S. (2021) · IEEE Access · doi:10.1109/access.2021.3057072 · Cited by 9
Fadzil, A. F. A., Khalid, N. E. A., & Ibrahim, S. (2021) · International Journal of Advanced Technology and Engineering Exploration · doi:10.19101/ijatee.2020.s1762117 · Cited by 9
Wang, Z., Weng, F., Liu, J., Cao, K., Hou, M., & Wang, J. (2021) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-021-01277-w · Cited by 9
Severoğlu, N., & Salor, Ö. (2020) · Conference record · doi:10.1109/ias44978.2020.9334839 · Cited by 9
Developing a Deep Learning-enabled Guide for the Visually Impaired
Shelton, A., & Ogunfunmi, T. (2020) · IEEE Global Humanitarian Technology Conference (GHTC) · doi:10.1109/ghtc46280.2020.9342873 · Cited by 9
Traffic Sign Recognition Based on ResNet-20 and Deep Mutual Learning
Huo, T., Fan, J., Li, X. S., Chen, H., Gao, B., & Li, X. (2020) · Chinese Automation Congress · doi:10.1109/cac51589.2020.9327282 · Cited by 9
Rezapour, M., & Ksaibati, K. (2021) · Signals · doi:10.3390/signals2010007 · Cited by 9
Compound identification via deep classification model for electron-ionization mass spectrometry
Hu, Q., Zhang, J., Chen, P., & Wang, B. (2021) · International Journal of Mass Spectrometry · doi:10.1016/j.ijms.2021.116540 · Cited by 9
Cloud-based ML Technologies for Visual Inspection: A Case Study in Manufacturing
Koppe, T., & Schatz, J. (2021) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2021.124 · Cited by 9
Deep convolutional neural network for preliminary in-field classification of lichen species
Galanty, A., Danel, T., Węgrzyn, M. H., Podolak, I., & Podolak, I. T. (2021) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2021.01.004 · Cited by 11
Neural network enhanced 3D turbo spin echo for MR intracranial vessel wall imaging
Zhou, Z., Chen, S., Balu, N., Chu, B., Zhao, X., Sun, J., Mossa‐Basha, M., Hatsukami, T. S., Börnert, P., & Yuan, C. (2021) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2021.01.004 · Cited by 9
Mustafid, A., Pamuji, M. M., & Helmiyah, S. (2020) · IJID (International Journal on Informatics for Development) · doi:10.14421/ijid.2020.09207 · Cited by 13
Residual‐wider convolutional neural network for image recognition
Li, G., Chen, W., & Mu, C. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2020.0726 · Cited by 9
WRGPruner: A new model pruning solution for tiny salient object detection
Jia, F., Wang, X., Guan, J., Li, H., Chen, Q., & Qi, S. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104143 · Cited by 9
AnCoins: Image-Based Automated Identification of Ancient Coins Through Transfer Learning Approaches
Kiourt, C., & Evangelidis, V. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68787-8_4 · Cited by 9
Plankton Recognition in Images with Varying Size
Bureš, J., Eerola, T., Lensu, L. T., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68780-9_11 · Cited by 9
Generation of Synthetic Data for Handwritten Word Alteration Detection
Dansena, P., Bag, S., & Pal, R. (2021) · IEEE Access · doi:10.1109/access.2021.3059342 · Cited by 10
A 4F optical diffuser system with spatial light modulators for image data augmentation
Li, B., Ersoy, O. K., Ma, C., Pan, Z., Wen, W., & Song, Z. (2021) · Optics Communications · doi:10.1016/j.optcom.2021.126859 · Cited by 10
Peng, J., Mei, X., Li, W., et al. (2021) · Remote Sensing · doi:10.3390/rs13040742 · Cited by 9
Dynamic proposal sampling for weakly supervised object detection
Jiang, W., Zhao, Z., Su, F., & Fang, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.02.018 · Cited by 9
Recognition of varying size scene images using semantic analysis of deep activation maps
Gupta, S., Dileep, A. D., & Thenkanidiyoor, V. (2021) · Machine Vision and Applications · doi:10.1007/s00138-021-01168-8 · Cited by 9
Unsupervised stereoscopic image retargeting via view synthesis and stereo cycle consistency losses
Fan, X., Lei, J., Liang, J., et al. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.02.079 · Cited by 9
Bias from the Wild Industry 4.0: Are We Really Classifying the Quality or Shotgun Series?
Rosati, R., Romeo, L., Cecchini, G., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68799-1_46 · Cited by 9
A deep learning-based binocular perception system
Sun, Z., Ma, C., Liang, W., Ran, M., & Pei, S. (2021) · Journal of Systems Engineering and Electronics · doi:10.23919/jsee.2021.000002 · Cited by 9
AIOT-Arch: Furthering Artificial Intelligence in Big Data IoT Applications
Said, S., Halawa, R. I., Hesham, O., Tabl, I. A., Maged, N., Youssef, S. M., & Elagamy, M. N. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1051/1/012008 · Cited by 9
Execution Time Modeling for CNN Inference on Embedded GPUs
Bouhali, N., Ouarnoughi, H., Niar, S., et al. (2021) · doi:10.1145/3444950.3447284 · Cited by 9
Kalyanraman, R. S., Chen, X., Wu, P.-Y., et al. (2021) · doi:10.2118/204078-ms · Cited by 9
Stacking Deep Learning for Early COVID-19 Vision Diagnosis
Hammam, A., Elmousalami, H. H., & Hassanien, A. E. (2020) · Studies in big data · doi:10.1007/978-3-030-55258-9_18 · Cited by 9
Ning, L., Cai, Z., Zhao, X., et al. (2021) · Ceramics International · doi:10.1016/j.ceramint.2021.03.145 · Cited by 9
Superpixel-Based Convolutional Neural Network for Georeferencing the Drone Images
Feng, S., Passone, L., & Schuster, G. T. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3065398 · Cited by 9
Learning Features for Writer Identification from Handwriting on Papyri
Nasir, S., & Siddiqi, I. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-71804-6_17 · Cited by 12
Embarrassingly Simple Binarization for Deep Single Imagery Super-Resolution Networks
Zhang, L., Lang, Z., Wei, W., & Zhang, Y. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3066906 · Cited by 10
Evaluation of deep learning model for human activity recognition
Bhat, O., & Khan, D. A. (2021) · Evolving Systems · doi:10.1007/s12530-021-09373-6 · Cited by 9
Thread Quality Classification of a Tapping Machine Based on Machine Learning
Wang, C.-S., Kao, I.-H., Hsu, Y.-W., et al. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3067216 · Cited by 9
Chen, G., Jia, W., Zhao, Y., Mao, Z., Lo, B. P. L., Anderson, A. K., Frost, G. S., Jobarteh, M. L., McCrory, M. A., Sazonov, E. E., et al. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.644712 · Cited by 11
da Silva, R. D. C., Jenkyn, T. R., & Carranza, V. A. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11040310 · Cited by 9
Deep learning in spine surgery
Ghaednia, H., Lans, A., Sauder, N., Shin, D. D., Grant, W. G., Chopra, R. R., Oosterhoff, J. H. F., Fourman, M. S., Schwab, J. H., & Tobert, D. G. (2021) · Seminars in Spine Surgery · doi:10.1016/j.semss.2021.100876 · Cited by 11
Xu, Y., Yan, M., Xu, C., Zhang, H., Liu, Y., & Xu, X. (2021) · IEEE Access · doi:10.1109/access.2021.3072623 · Cited by 10
XCloud-pFISTA: A Medical Intelligence Cloud for Accelerated MRI
Zhou, Y., Qian, C., Guo, Y., Wang, Z., Wang, J., Qu, B., Guo, D., You, Y., & Qu, X. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630813 · Cited by 11
Feature Selection Module for CNN Based Object Detector
Ma, Y., & Zhang, S. (2021) · IEEE Access · doi:10.1109/access.2021.3073565 · Cited by 10
Pseudo-label growth dictionary pair learning for crowd counting
Liu, W., Wang, H., Luo, H., et al. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02274-w · Cited by 9
Merged 1D-2D Deep Convolutional Neural Networks for Nerve Detection in Ultrasound Images
Alkhatib, M., Hafiane, A., & Vieyres, P. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412988 · Cited by 9
MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains
Wang, Y., González-García, A., Wu, C., Herranz, L., Khan, F. S., Jui, S., Yang, J., & van de Weijer, J. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01882-y · Cited by 11
Computer aided drug discovery review for infectious diseases with case study of anti-Chagas project
Yasuo, N., Ishida, T., & Sekijima, M. (2021) · Parasitology International · doi:10.1016/j.parint.2021.102366 · Cited by 10
Learning from Noisy Complementary Labels with Robust Loss Functions
Ishiguro, H., ISHIDA, T., & Sugiyama, M. (2022) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2021edp7035 · Cited by 9
Artificial intelligence in child abuse imaging
Sorensen, J. I. L., Nikam, R. M., & Choudhary, A. K. (2021) · Pediatric Radiology · doi:10.1007/s00247-021-05073-0 · Cited by 9
An online guided tuning approach to run CNN pipelines on edge devices
Soomro, P. N., Abduljabbar, M., Castrillón, J., et al. (2021) · ACM International Conference on Computing Frontiers (CF) · doi:10.1145/3457388.3458662 · Cited by 9
Multi-target detection method based on YOLOv4 convolutional neural network
Liu, J. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1883/1/012075 · Cited by 9
Automated Biometric Identification using Dorsal Hand Images and Convolutional Neural Networks
Mohaghegh, M., & Payne, A. M. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1880/1/012014 · Cited by 9
Classification Between Pedestrians and Motorcycles using FasterRCNN Inception and SSD MobileNetv2
Giron, N. N. F., Billones, R. K. C., Fillone, A. M., et al. (2020) · IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) · doi:10.1109/hnicem51456.2020.9400113 · Cited by 9
Medical image management and analysis system based on web for fungal keratitis images
Hou, H., Cao, Y., Cui, X., Liu, Z., Xu, H., Wang, C., Zhang, W., Zhang, Y., Fang, Y., Geng, Y., et al. (2021) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2021183 · Cited by 9
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
