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 45,001–45,100 of 108,439
Oki, T., & KIZAWA, S. (2021) · 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-xliii-b3-2021-887-2021 · Cited by 14
Integrated deep learning for self-driving robotic cars
Gonsalves, T., & Upadhyay, J. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-85498-6.00010-1 · Cited by 14
Naik, S., Debnath, S., & Justin, V. (2021) · International Conference for Emerging Technology (INCET) · doi:10.1109/incet51464.2021.9456394 · Cited by 18
Cross Modality Knowledge Distillation for Multi-modal Aerial View Object Classification
Yang, L., & Xu, K. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00048 · Cited by 14
Enomoro, S., & Eda, T. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i8.16900 · Cited by 15
Learning Image Quality Assessment by Reinforcing Task Amenable Data Selection
Saeed, S. U., Fu, Y., Baum, Z. M. C., Yang, Q., Rusu, M., Fan, R. E., Sonn, G. A., Barratt, D. C., & Hu, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-78191-0_58 · Cited by 15
Stasenko, N., Chernova, E., Shadrin, D., et al. (2021) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc50364.2021.9460071 · Cited by 14
Automatic orientation detection of abstract painting
Bai, R., & Guo, X. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107240 · Cited by 14
A Multi-Task Deep Feature Selection Method for Brain Imaging Genetics
Yu, C., Zhang, S., Shang, M., Guo, L., Han, J., & Du, L. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3294413 · Cited by 15
Classification of Airborne Laser Scanning Point Cloud Using Point-Based Convolutional Neural Network
Zhu, J., Sui, L., Zang, Y., et al. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi10070444 · Cited by 14
Convolutional neural network-based ensemble methods to recognize Bangla handwritten character
Shibly, M. M. A., Tisha, T. A., Tani, T. A., et al. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.565 · Cited by 14
Hou, Y., Hua, Y., Zhou, D., Wang, P., Ge, H., Zhang, J., & Zhang, Q. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06239-5 · Cited by 14
Convolutional neural network architectures
Huang, S.-C., & Le, T.-H. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-90198-7.00001-x · Cited by 15
Pauloski, J. G., Huang, Q., Huang, L., Venkataraman, S., Chard, K., Foster, I. T., & Zhang, Z. (2021) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3458817.3476152 · Cited by 21
Orfao, J., & van der Haar, D. T. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-80432-9_3 · Cited by 14
Multi-scale network (MsSG-CNN) for joint image and saliency map learning-based compression
Mishra, D., Singh, S. K., Singh, R. K., & Kedia, D. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.012 · Cited by 15
RaScaNet: Learning Tiny Models by Raster-Scanning Images
Yoo, J., Lee, D., Son, C., Jung, S., Yoo, B., Choi, C., Han, J.-J., & Han, B. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01346 · Cited by 14
Duan, Y., Zhang, J., & Gu, X. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.713545 · Cited by 15
Shinde, S., Tupe-Waghmare, P., Chougule, T., Saini, J. S., & Ingalhalikar, M. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.622 · Cited by 16
Louati, H., Bechikh, S., Louati, A., Aldaej, A., & Saïd, L. B. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-79457-6_11 · Cited by 15
Zhou, Y., Maître, R., Hupel, M., Trotoux, G., Penguilly, D., Mariette, F., Bousset, L., Chèvre, A., & Parisey, N. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106303 · Cited by 16
Quantifying plant mimesis in fossil insects using deep learning
Fan, L., Xu, C., Jarzembowski, E. A., & Cui, X. (2021) · Historical Biology · doi:10.1080/08912963.2021.1952199 · Cited by 12
Understanding and Simplifying Perceptual Distances
Amir, D., & Weiss, Y. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01205 · Cited by 15
Deep multi-task mining Calabi–Yau four-folds
Erbin, H., Finotello, R., Schneider, R., & Tamaazousti, M. (2021) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ac37f7 · Cited by 19
Taghizadegan, Y., Dabanloo, N. J., Maghooli, K., et al. (2021) · Medical Hypotheses · doi:10.1016/j.mehy.2021.110659 · Cited by 14
Instance reweighting and dynamic distribution alignment for domain adaptation
Azarkesht, M., & Afsari, F. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03426-z · Cited by 15
Semi-Supervised Few-Shot Class-Incremental Learning
Cui, Y., Xiong, W., Tavakolian, M., & Liu, L. (2021) · 2021 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip42928.2021.9506346 · Cited by 16
Weeds detection efficiency through different convolutional neural networks technology
Tannouche, A., Gaga, A., Boutalline, M., et al. (2021) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v12i1.pp1048-1055 · Cited by 14
Deep Semisupervised Multiview Learning With Increasing Views
Hu, P., Peng, X., Zhu, H., Zhen, L., Lin, J., Yan, H., & Peng, D. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3093626 · Cited by 14
Zaman, F. A., Ponnapureddy, R., Wang, Y. G., Chang, A., Cadaret, L., Abdelhamid, A., Roy, S. D., Makan, M., Zhou, R., Jayanna, M. B., et al. (2021) · EClinicalMedicine · doi:10.1016/j.eclinm.2021.101115 · Cited by 14
Network Threat Detection Based on Group CNN for Privacy Protection
Xu, Y., Zhang, X., Lu, C., Qiu, Z., Bi, C., Lai, Y., Qiu, J., & Zhang, H. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/3697536 · Cited by 17
Avramov-Zamurovic, S., Nelson, C. L., & Esposito, J. M. (2021) · Journal of Modern Optics · doi:10.1080/09500340.2021.1970263 · Cited by 13
Self-supervised Learning for Reading Activity Classification
Islam, M. R., Sakamoto, S., Yamada, Y., Vargo, A. W., Iwata, M., Iwamura, M., & Kise, K. (2021) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3478088 · Cited by 18
Statistical Certification of Acceptable Robustness for Neural Networks
Huang, C., Hu, Z., Huang, X., & Pei, K. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86362-3_7 · Cited by 15
Kim, S.-H., Hahn, J., & Youn, B. D. (2021) · IEEE Access · doi:10.1109/access.2021.3112678 · Cited by 15
Deep Learning-Based Ground Target Detection and Tracking for Aerial Photography from UAVs
Wang, K., Meng, Z., & Wu, Z. (2021) · Applied Sciences · doi:10.3390/app11188434 · Cited by 14
Zhao, X., Yan, H., Hu, Z., et al. (2021) · IISE Transactions on Healthcare Systems Engineering · doi:10.1080/24725579.2021.1982081 · Cited by 14
Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution
Xu, B., & Yin, H. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534213 · Cited by 15
Heart-Darts: Classification of Heartbeats Using Differentiable Architecture Search
Lv, J., Ye, Q., Sun, Y., et al. (2021) · doi:10.1109/ijcnn52387.2021.9534184 · Cited by 14
Automatically classifying crime scene images using machine learning methodologies
Abraham, J., Ng, R., Morelato, M., Tahtouh, M., & Roux, C. P. (2021) · Forensic Science International Digital Investigation · doi:10.1016/j.fsidi.2021.301273 · Cited by 14
An Improved Deep Learning Approach for Retrieving Outfalls Into Rivers From UAS Imagery
Huang, Y., Wu, C., Yang, H., Zhu, H., Chen, M., & Yang, J. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3113901 · Cited by 16
Ann, K., Jang, Y., Shim, H., & Chang, H. (2021) · IEEE Access · doi:10.1109/access.2021.3116034 · Cited by 14
Kim, N., Park, H., Lee, D., et al. (2021) · IEEE Transactions on Computers · doi:10.1109/tc.2021.3092205 · Cited by 14
Dynamic DNN Decomposition for Lossless Synergistic Inference
Zhang, B., Tian, X., Zhang, H., Li, T., Zhu, S., & Gu, J. (2021) · IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW) · doi:10.1109/icdcsw53096.2021.00010 · Cited by 19
A CNN Approach for Optimal Power Flow Problem for Distribution Network
Jia, Y., & Bai, X. (2021) · Power System and Green Energy Conference (PSGEC) · doi:10.1109/psgec51302.2021.9542526 · Cited by 14
Deep embeddings and logistic regression for rapid active learning in histopathological images
Jiao, Y., Yuan, J., Qiang, Y., & Fei, S. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106464 · Cited by 14
Xu, J., Sun, H., Qi, Q., et al. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475705 · Cited by 14
Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation
Negassi, M., Wagner, D., & Reiterer, A. (2022) · Algorithms · doi:10.3390/a15050165 · Cited by 20
Kartal, S. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101467 · Cited by 16
Decoupled self-supervised label augmentation for fully-supervised image classification
Gao, W., Wu, M., Lam, S.-K., Xia, Q., & Zou, J. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107605 · Cited by 15
On the receptive field misalignment in CAM-based visual explanations
Xia, P., Niu, H., Li, Z., & Li, B. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.10.024 · Cited by 14
Reconstructed phase space portraits for detecting brain diseases using deep learning
Ilakiyaselvan, N., Khan, A. N., & Shahina, A. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103278 · Cited by 15
Basic Artificial Intelligence Techniques
Kalpathy–Cramer, J., Patel, J., Bridge, C. P., & Chang, K. (2021) · Radiologic Clinics of North America · doi:10.1016/j.rcl.2021.06.005 · Cited by 15
Zhang, H., & Cheng, W. (2021) · Journal of Electrical and Computer Engineering · doi:10.1155/2021/9979634 · Cited by 15
Crack recognition automation in concrete bridges using Deep Convolutional Neural Networks
Zoubir, H., Rguig, M., & Elaroussi, M. (2021) · MATEC Web of Conferences · doi:10.1051/matecconf/202134903014 · Cited by 15
Evaluating Performance, Power and Energy of Deep Neural Networks on CPUs and GPUs
Sun, Y., Ou, Z., Chen, J., et al. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-7443-3_12 · Cited by 14
Using Artificial Intelligence and IoT for Constructing a Smart Trash Bin
Lâm, K. N., Huynh, N. H., Ngoc, N. B., et al. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-8062-5_29 · Cited by 14
Proteome‐Scale Drug‐Target Interaction Predictions: Approaches and Applications
MacKinnon, S. S., Tonekaboni, S. A. M., & Windemuth, A. (2021) · Current Protocols · doi:10.1002/cpz1.302 · Cited by 13
Application of Machine Vision in Classifying Gait Frailty Among Older Adults
Liu, Y., He, X., Wang, R., Teng, Q., Hu, R. B., Qing, L., Wang, Z., He, X., Yin, B., Mou, Y., et al. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.757823 · Cited by 19
Dong, S., Li, Y., Zhu, P. Y., Pei, X., Pan, X., Xu, X., Liu, L., Xing, B., & Hu, X. (2021) · Measurement Science and Technology · doi:10.1088/1361-6501/ac39d1 · Cited by 14
Deep Transfer Learning for Pancreatic Cancer Detection
Baldota, S., Sharma, S., & Malathy, C. (2021) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt51525.2021.9580000 · Cited by 14
Enhanced VGG19 Model for Accident Detection and Classification from Video
Bouhsissin, S., Sael, N., & Benabbou, F. (2021) · International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA) · doi:10.1109/icdata52997.2021.00017 · Cited by 14
Liang, C., Fang, P., Huang, H., & Lo, C. (2021) · Cancers · doi:10.3390/cancers13225787 · Cited by 15
Li, X., Zhang, J., Chen, W., Chen, Y., Zhang, C., Xiang, W., & Li, D. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106533 · Cited by 16
Identification of Enzymes-specific Protein Domain Based on DDE, and Convolutional Neural Network
Sikander, R., Wang, Y., Ghulam, A., & Wu, X. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.759384 · Cited by 14
Liu, H., Shargh, A. K., & Abdolrahim, N. (2021) · Materialia · doi:10.1016/j.mtla.2021.101275 · Cited by 13
Forecasting nationwide passenger flows at city-level via a spatiotemporal deep learning approach
He, Y., Zhao, Y., Luo, Q., et al. (2021) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2021.126603 · Cited by 14
Triple descent and the two kinds of overfitting: where and why do they appear?*
d’Ascoli, S., Sagun, L., & Biroli, G. (2021) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/ac3909 · Cited by 53
Zhao, K., Zhang, M., Xie, Z., Xu, Y., Wu, S., Liao, P., Lu, H., Shen, W., Fu, C.-C., Cui, H., et al. (2021) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28025 · Cited by 14
Zhu, Z., Lv, D., Zhang, X., et al. (2021) · Contrast Media & Molecular Imaging · doi:10.1155/2021/2015780 · Cited by 14
Multimodal Deep Learning via Late Fusion for Non-Destructive Papaya Fruit Maturity Classification
Manliguez, C., & Chiang, J. Y. (2021) · International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) · doi:10.1109/cce53527.2021.9633060 · Cited by 16
Tomato Plant Leaf Disease Detection using CNN
Kodali, R. K., & Gudala, P. (2021) · IEEE Region Humanitarian Technology Conference · doi:10.1109/r10-htc53172.2021.9641655 · Cited by 14
GlassNet: Label Decoupling‐based Three‐stream Neural Network for Robust Image Glass Detection
Zheng, C., Shi, D., Yan, X., Liang, D., Wei, M., Yang, X. S., Guo, Y., & Xie, H. (2022) · Computer Graphics Forum · doi:10.1111/cgf.14441 · Cited by 16
Zhu, Y., Long, L., Wang, J., Yan, J., & Wang, X. (2022) · Cognitive Computation · doi:10.1007/s12559-021-09980-0 · Cited by 13
Ma, D., Wu, R., Li, Z., et al. (2022) · Chinese Journal of Chemical Engineering · doi:10.1016/j.cjche.2021.11.023 · Cited by 14
Bali, A., & Mansotra, V. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0121269 · Cited by 14
Learn more from less: Generalized zero-shot learning with severely limited labeled data
Lu, Z., Lu, Z., Yu, Y., & Wang, Z. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.01.007 · Cited by 14
Zhai, Q., Fan, B., Zhang, B., Li, J., & Liu, J. (2022) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-022-00683-x · Cited by 13
A Deep Learning Model Compression and Ensemble Approach for Weed Detection
Ofori, M. Q., El-Gayar, O., O’Brien, A., & Noteboom, C. (2022) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2022.138 · Cited by 14
Kanno, Y., & Kaneko, H. (2022) · ACS Omega · doi:10.1021/acsomega.1c06607 · Cited by 15
Park, C., Kim, B., Kim, Y., et al. (2022) · Sensors · doi:10.3390/s22030816 · Cited by 14
Patoloji Görüntülerinin Derin Öğrenme Yöntemleri İle Sınıflandırılması
KARAKURT, M., & İşeri, İ. (2022) · European Journal of Science and Technology · doi:10.31590/ejosat.1011091 · Cited by 16
SAB Net: A Semantic Attention Boosting Framework for Semantic Segmentation
Ding, X., Shen, C., Zeng, T., & Peng, Y. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3144003 · Cited by 21
Evaluation of Transfer Learning based Deep Learning architectures for Waste Classification
Singh, S., Gautam, J., Rawat, S. S., Gupta, V. K., Kumar, G., & Verma, L. P. (2021) · International Symposium on Advanced Electrical and Communication Technologies (ISAECT) · doi:10.1109/isaect53699.2021.9668454 · Cited by 16
A novel micro-expression detection algorithm based on BERT and 3DCNN
Zhou, Y., Song, Y., Chen, L., Chen, Y., Ben, X., & Cao, Y. (2022) · Image and Vision Computing · doi:10.1016/j.imavis.2022.104378 · Cited by 14
DA-GAN: Dual Attention Generative Adversarial Network for Cross-Modal Retrieval
Cai, L., Zhu, L., Zhang, H., & Zhu, X. (2022) · Future Internet · doi:10.3390/fi14020043 · Cited by 14
Deep Learning Algorithms Enabling Event Detection: A Review
Nakkach, C., Zrelli, A., & Ezzeddine, T. (2022) · Advances in intelligent systems research/Advances in Intelligent Systems Research · doi:10.2991/aisr.k.220201.030 · Cited by 14
Wan, T., Ji, H., Xiong, W., et al. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-022-02162-x · Cited by 14
Jiang, Z., Wang, Y., Shi, C., Wu, Y., Hu, R., Chen, S., Hu, S., Wang, X., & Qiu, B. (2022) · Human Brain Mapping · doi:10.1002/hbm.25813 · Cited by 18
MLCRNet: Multi-Level Context Refinement for Semantic Segmentation in Aerial Images
Huang, Z., Zhang, Q., & Zhang, G. (2022) · Remote Sensing · doi:10.3390/rs14061498 · Cited by 14
Pécot, T., Cuitiño, M. C., Johnson, R. H., Timmers, C. D., & Leone, G. W. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009949 · Cited by 17
A Semi-Supervised Semantic Segmentation Method for Blast-Hole Detection
Zhang, Z., Deng, H., Liu, Y., Xu, Q., & Liu, G. (2022) · Symmetry · doi:10.3390/sym14040653 · Cited by 14
Performance evaluation of salient object detection techniques
Ahmed, K., Gad, M. A., & Aboutabl, A. E. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12567-y · Cited by 16
Plastic Waste Classification Using CNN for Supporting 3R's Principle
Gothai, E., Thamilselvan, R., Natesan, P., Keerthivasan, M., Kabinesh, K., & Ruban, D. K. (2022) · International Conference on Computer Communication and Informatics (ICCCI) · doi:10.1109/iccci54379.2022.9740902 · Cited by 14
Hua, C., Zou, X., Ling, Y., et al. (2022) · Computers & Graphics · doi:10.1016/j.cag.2022.03.011 · Cited by 14
Hybrid deep learning model for answering visual medical questions
Gasmi, K. (2022) · The Journal of Supercomputing · doi:10.1007/s11227-022-04474-8 · Cited by 14
Yang, R., Yu, J., Yin, J., Liu, K., & Xu, S. (2022) · IET Image Processing · doi:10.1049/ipr2.12503 · Cited by 15
Zhao, S., Cahill, D., Li, S., et al. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106295 · Cited by 13
Interpretable model-driven projected gradient descent network for high-quality fDOT reconstruction
Hua, Y., Jiang, Y., Liu, K., et al. (2022) · Optics Letters · doi:10.1364/ol.459456 · Cited by 14
Panigrahi, S., & Raju, U. S. N. (2022) · Optik · doi:10.1016/j.ijleo.2022.169061 · Cited by 14
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
