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 51,701–51,800 of 108,439
On-device facial verification using NUF-Net model of deep learning
Termritthikun, C., Jamtsho, Y., & Muneesawang, P. (2019) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2019.07.018 · Cited by 10
Deep Learning for Autonomous Vehicle Control: Algorithms, State-of-the-Art, and Future Prospects
Kuutti, S., Fallah, S., Bowden, R., & Barber, P. (2019) · Synthesis lectures on advances in automotive technology · doi:10.2200/s00932ed1v01y201906aat008 · Cited by 29
Ahmed, A. M., Duran, O., Zweiri, Y., & Smith, M. J. (2019) · Remote Sensing · doi:10.3390/rs11161938 · Cited by 10
Weakly Supervised Object Discovery by Generative Adversarial & Ranking Networks
Diba, A., Sharma, V., Stiefelhagen, R., & Gool, L. V. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00086 · Cited by 17
3TP-CNN: Radiomics and Deep Learning for Lesions Classification in DCE-MRI
Gravina, M., Marrone, S., Piantadosi, G., Sansone, M., & Sansone, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30645-8_60 · Cited by 10
Automatic Identification of Overpass Structures: A Method of Deep Learning
Li, H., Hu, M., & Huang, Y. (2019) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi8090421 · Cited by 10
Multi-Scale Receptive Field Detection Network
Cui, H., & Wei, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2942077 · Cited by 10
Improving Route Choice Models by Incorporating Contextual Factors via Knowledge Distillation
Liu, Q., Mukhopadhyay, S., Zhu, Y., et al. (2019) · doi:10.1109/ijcnn.2019.8852482 · Cited by 10
Lin, L., Chen, C., Yang, J., et al. (2019) · Electronics · doi:10.3390/electronics8101112 · Cited by 10
Mirzaeian, A., Homayoun, H., & Sasan, A. (2019) · International Conference on ReConFigurable Computing and FPGAs (ReConFig) · doi:10.1109/reconfig48160.2019.8994751 · Cited by 10
Medical image learning from a few/few training samples: Melanoma segmentation study
Asaeikheybari, G., Green, J., Qian, X., Jiang, H., & Huang, M.-C. (2019) · Smart Health · doi:10.1016/j.smhl.2019.100088 · Cited by 10
Chervyakov, N. I., Lyakhov, P. A., Nagornov, N. N., Valueva, M. V., & Valuev, G. (2019) · Computer Optics · doi:10.18287/2412-6179-2019-43-5-857-868 · Cited by 10
Convex Optimization for Shallow Neural Networks
Ergen, T., & Pilancı, M. (2019) · Allerton Conference on Communication, Control, and Computing · doi:10.1109/allerton.2019.8919769 · Cited by 10
Enet-CRF-Lidar: Lidar and Camera Fusion for Multi-Scale Object Recognition
Deng, Q., Li, X., Ni, P., Li, H., & Zheng, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2956492 · Cited by 11
Graph convolutional neural network for multi-scale feature learning
Edwards, M., Xie, X., Palmer, R. I., Tam, G. K. L., Alcock, R., & Roobottom, C. A. (2019) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.102881 · Cited by 9
Child Face Recognition with Deep Learning
Oo, S. L. M., & Oo, A. N. (2019) · International Conference on Advanced Information Technologies (ICAIT) · doi:10.1109/aitc.2019.8921152 · Cited by 10
Template Creation for High-Resolution Computed Tomography Scans of the Lung in R Software
Ryan, S., Vestal, B. E., Maier, L. A., Carlson, N. E., & Muschelli, J. (2019) · Academic Radiology · doi:10.1016/j.acra.2019.10.030 · Cited by 9
Gao, H., Cai, L., & Ji, S. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5805 · Cited by 14
Neural Comment Generation for Source Code with Auxiliary Code Classification Task
Chen, M., & Wan, X. (2019) · Asia-Pacific Software Engineering Conference (APSEC) · doi:10.1109/apsec48747.2019.00076 · Cited by 9
A Deeper Look at Human Visual Perception of Images
Fan, S., Koenig, B. L., Zhao, Q., & Kankanhalli, M. (2020) · SN Computer Science · doi:10.1007/s42979-019-0061-5 · Cited by 10
Towards Safety-Risk Prediction of CBTC Systems With Deep Learning and Formal Methods
Liu, J., Li, Q., Yan, Z., Han, J., & Sun, J. (2020) · IEEE Access · doi:10.1109/access.2020.2967634 · Cited by 11
Hierarchical Refined Local Associations for Robust Person Re-Identification
Perwaiz, N., Fraz, M. M., & Shahzad, M. (2019) · International Conference on Robotics and Automation in Industry (ICRAI) · doi:10.1109/icrai47710.2019.8967389 · Cited by 10
Dog Breed Identification Using Convolutional Neural Networks on Android
Bhavani, D. D., Quadri, M. H. S., & Reddy, Y. R. (2019) · CVR Journal of Science & Technology · doi:10.32377/cvrjst1711 · Cited by 10
Hasegawa, T., & Koshino, M. (2019) · International Conference on Computational Intelligence and Intelligent Systems (CIIS) · doi:10.1145/3372422.3372439 · Cited by 9
SPEC2: SPECtral SParsE CNN Accelerator on FPGAs
Niu, Y., Zeng, H., Srivastava, A., Lakhotia, K., Kannan, R., Wang, Y., & Prasanna, V. K. (2019) · IEEE International Conference on High Performance Computing, Data, and Analytics · doi:10.1109/hipc.2019.00033 · Cited by 12
Liu, D., Zhao, J., Xi, A., Wang, C., Huang, X., Lai, K., & Liu, C. (2020) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2020.08641 · Cited by 11
Tobacco Plant Detection in RGB Aerial Images
Sun, X., Peng, J., Shen, Y., et al. (2020) · Agriculture · doi:10.3390/agriculture10030057 · Cited by 10
Saliency detection using a deep conditional random field network
Qiu, W., Gao, X., & Han, B. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107266 · Cited by 10
An energy-efficient deep convolutional neural networks coprocessor for multi-object detection
Wu, Y., Wang, J., Qian, K., Liu, Y., Guo, R., Hu, S. G., Yu, Q., Chen, T. P., Liu, Y., & Rong, L. (2020) · Microelectronics Journal · doi:10.1016/j.mejo.2020.104737 · Cited by 10
Mabu, S., Nakayama, Y., & Kuremoto, T. (2020) · Journal of Signal Processing · doi:10.2299/jsp.24.61 · Cited by 11
System Independent Machine Fault Diagnosis Using Convolutional Neural Networks
Sreekumar, K. T., Kumar, C. S., & Ramachandran, K. I. (2018) · IEEE India Conference · doi:10.1109/indicon45594.2018.8987125 · Cited by 10
Random Forest Boosted CNN: An Empirical Technique for Plant Classification
Banerjee, S., & Pamula, R. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-2188-1_20 · Cited by 11
Attentional Fused Temporal Transformation Network for Video Action Recognition
Yang, K., Wang, Z., Dai, H., et al. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053394 · Cited by 10
Processing Systems for Deep Learning Inference on Edge Devices
Véstias, M. P. (2020) · Internet of things · doi:10.1007/978-3-030-44907-0_9 · Cited by 11
Ding, X., Li, B., Li, Y., et al. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2992276 · Cited by 10
An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration
Salami, B., Onural, E. B., Yüksel, İ. E., Koç, F., Ergin, O., Kestelman, A. C., Ünsal, O., Sarbazi‐Azad, H., & Mutlu, O. (2020) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn48063.2020.00032 · Cited by 57
Effective node selection technique towards sparse learning
Ibrokhimov, B., Hur, C., & Kang, S. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01720-5 · Cited by 10
Discrimination-Aware Domain Adversarial Neural Network
Wang, Y.-Y., Gu, J., Wang, C., Chen, S., & Xue, H. (2020) · Journal of Computer Science and Technology · doi:10.1007/s11390-020-9969-4 · Cited by 10
Virtual Relay Selection in LTE-V: A Deep Reinforcement Learning Approach to Heterogeneous Data
Du, X., Nguyen, H. V., Jiang, C., Li, Y., Yu, F. R., & Han, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2997729 · Cited by 10
Lin, Z., Jia, J., Gao, W., & Huang, F. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.05.050 · Cited by 10
Park, C.-S., Chae, H.-W., & Song, J.-B. (2020) · International Journal of Control Automation and Systems · doi:10.1007/s12555-019-0891-x · Cited by 11
Learning Surrogates via Deep Embedding
Patel, Y., Hodaň, T., & Matas, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58577-8_13 · Cited by 9
Learning Robust Scene Classification Model with Data Augmentation Based on Xception
Chen, H., Yang, Y., & Zhang, S. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1575/1/012009 · Cited by 10
Generating Face Images With Attributes for Free
Liu, Y., Sun, Q., He, X., Liu, A.-A., Su, Y., & Chua, T. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3007790 · Cited by 9
Comparing human and automated approaches to visual storytelling
Braun, S., Starr, K., & Laaksonen, J. T. (2020) · Routledge eBooks · doi:10.4324/9781003052968-9 · Cited by 10
An Artificial Intelligence Approach for Time Series Next Generation Applications
Dridi, A., Khedher, H. I., Moungla, H., & Afifi, H. (2020) · International Conference on Communications · doi:10.1109/icc40277.2020.9148931 · Cited by 10
Real-time chinese traffic warning signs recognition based on cascade and CNN
Gao, Y., & Xiao, G. (2020) · Journal of Real-Time Image Processing · doi:10.1007/s11554-020-01003-9 · Cited by 10
Black Magic in Deep Learning: How Human Skill Impacts Network Training
Anand, K., Wang, Z., Loog, M., & van Gemert, J. C. (2020) · British Machine Vision Conference (BMVC) · doi:10.5244/c.34.137 · Cited by 17
Does Removing Pooling Layers from Convolutional Neural Networks Improve Results?
dos Santos, C. F. G., Moreira, T. P., Colombo, D., & Papa, J. P. (2020) · SN Computer Science · doi:10.1007/s42979-020-00295-9 · Cited by 11
Zieliński, S. K., Lee, H., Antoniuk, P., et al. (2020) · Applied Sciences · doi:10.3390/app10175956 · Cited by 10
Medeiros, A. G., Ohata, E. F., Silva, F. H. S., Rêgo, P. A. L., & Filho, P. P. R. (2020) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms49503.2020.00119 · Cited by 10
DiscoStyle: Multi-level Logistic Ranking for Personalized Image Style Preference Inference
He, Z., Zhang, L., & Liu, F. (2020) · Machine Intelligence Research · doi:10.1007/s11633-020-1244-1 · Cited by 10
CIFAR-10 Image Classification Based on Convolutional Neural Network
Lv, X. (2020) · Frontiers in Signal Processing · doi:10.22606/fsp.2020.44004 · Cited by 10
Xiang, Z. J., Miller, D. J., Wang, H., & Kesidis, G. (2020) · Machine learning for signal processing ... · doi:10.1109/mlsp49062.2020.9231861 · Cited by 12
Wilbur, D. C., Pettus, J. R., Smith, M. L., Cornell, L. D., Andryushkin, A., Wingard, R., & Wirch, E. (2020) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_49_20 · Cited by 10
Utilizing Multimodal Feature Consistency to Detect Adversarial Examples on Clinical Summaries
Wang, W., Park, Y. H., Lee, T., Molloy, I., Tang, P., & Xiong, L. (2020) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2020.clinicalnlp-1.29 · Cited by 11
Deep Kernel machines: a survey
Nair, N. K., Afzal, A. L., & Asharaf, S. (2020) · Pattern Analysis and Applications · doi:10.1007/s10044-020-00933-1 · Cited by 10
Efficient Transfer Learning Combined Skip-Connected Structure for Masked Face Poses Classification
Chen, S., Liu, W., & Zhang, G. (2020) · IEEE Access · doi:10.1109/access.2020.3039862 · Cited by 10
Automatic Leaf Species Recognition Using Deep Neural Network
Kumar, D., & Verma, C. (2020) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-7804-5_2 · Cited by 10
Ge, H., Duan, K., Wang, J., et al. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.597406 · Cited by 10
Tackling the challenges of bioimage analysis
Pelt, D. M. (2020) · eLife · doi:10.7554/elife.64384 · Cited by 11
Estimating Traffic in Urban Areas from Very-High Resolution Aerial Images
Reksten, J. H., & Salberg, A.-B. (2020) · International Journal of Remote Sensing · doi:10.1080/01431161.2020.1815891 · Cited by 10
Improving Neural Network With Uniform Sparse Connectivity
Luo, W. (2020) · IEEE Access · doi:10.1109/access.2020.3040943 · Cited by 10
Chen, W., Niu, J.-S., Liu, I.-P., Liu, B.-Y., Cheng, S., Lin, K., & Liu, W.-C. (2020) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2020.3041567 · Cited by 9
Overlap Training to Mitigate Inconsistencies Caused by Image Tiling in CNNs
An, Y., Ye, Q., Guo, J., & Dong, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-63799-6_3 · Cited by 11
Efficient Buffer Overflow Detection on GPU
Di, B., Sun, J., Chen, H., et al. (2020) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.3042965 · Cited by 10
Detecting Trojaned DNNs Using Counterfactual Attributions
Sikka, K., Sur, I., Roy, A., Divakaran, A., & Jha, S. (2023) · IEEE International Conference on Assured Autonomy (ICAA) · doi:10.1109/icaa58325.2023.00019 · Cited by 15
Place Classification Algorithm Based on Semantic Segmented Objects
Yeo, W.-H., Heo, Y.-J., Choi, Y.-J., et al. (2020) · Applied Sciences · doi:10.3390/app10249069 · Cited by 10
Phong, B. H., Dat, L. T., Yen, N. T., et al. (2020) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse50997.2020.9287693 · Cited by 10
Almabdy, S., & Elrefaei, L. A. (2021) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/100144 · Cited by 10
MemoSYS at SemEval-2020 Task 8: Multimodal Emotion Analysis in Memes
Bejan, I. (2020) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2020.semeval-1.155 · Cited by 10
A multi-branch ResNet with discriminative features for detection of replay speech signals
Cheng, X., Xu, M., & Zheng, T. F. (2020) · APSIPA Transactions on Signal and Information Processing · doi:10.1017/atsip.2020.26 · Cited by 10
Network traffic classification method based on improved deep convolutional neural network
Zhang, X., Cheng, G., & ZHANG, W. (2020) · Scientia Sinica Informationis · doi:10.1360/ssi-2019-0213 · Cited by 10
Akbal, A. (2021) · Applied Acoustics · doi:10.1016/j.apacoust.2020.107888 · Cited by 10
Web Powered CT Scan Diagnosis for Brain Hemorrhage using Deep Learning
Hebbar, N., Patil, H. Y., & Agarwal, K. (2020) · IEEE Conference on Information and Communication Technology (CICT) · doi:10.1109/cict51604.2020.9312098 · Cited by 10
Sawant, G., & Bharadi, V. A. (2020) · doi:10.1109/iccdw45521.2020.9318715 · Cited by 10
Joung, S., Kim, Y., Kim, J., Park, J., Jang, M., Lee, J., Kim, C.-J., Lee, M. S., & Lim, J.-M. (2021) · Applied Radiation and Isotopes · doi:10.1016/j.apradiso.2021.109593 · Cited by 10
Optoelectronic convolutional neural networks based on time-stretch method
Zang, Y., Chen, M., Yang, S., & Chen, H. (2021) · Science China Information Sciences · doi:10.1007/s11432-020-2998-1 · Cited by 10
A specific perspective: Subway driver behaviour recognition using CNN and time‐series diagram
Huang, S., Yang, L., Chen, W., et al. (2021) · IET Intelligent Transport Systems · doi:10.1049/itr2.12032 · Cited by 10
Distributed Deep Learning of ResNet50 and VGG16 with Pipeline Parallelism
Takisawa, N., Yazaki, S., & Ishihata, H. (2020) · International Symposium on Computing and Networking Workshops (CANDARW) · doi:10.1109/candarw51189.2020.00036 · Cited by 10
Castellano, G., & Vessio, G. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68796-0_35 · Cited by 12
Marine Pollution Detection based on Deep Learning and Optical Flow
Wu, C.-H., Hsieh, J.-W., Wang, C.-Y., & Ho, C.-H. (2020) · International Computer Symposium · doi:10.1109/ics51289.2020.00081 · Cited by 11
Smart Elevator Cotrol System Based on Human Hand Gesture Recognition
Le, S., Lei, Q., Wei, X., Zhong, J., Wang, Y., Jimin, Z., & Wang, W. (2020) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc51575.2020.9344869 · Cited by 11
Qin, Y., Luo, H., Zhao, F., Wang, C., & Fang, Y. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3060761 · Cited by 11
Intelligent unmanned aerial vehicle (UAV) system for aircraft surface inspection
He, B., Huang, B., Lin, Y., & Wu, L. (2020) · International Forum on Electrical Engineering and Automation (IFEEA) · doi:10.1109/ifeea51475.2020.00073 · Cited by 10
A comparative research on clothing images classification based on neural network models
Wang, D. (2020) · IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT) · doi:10.1109/iccasit50869.2020.9368530 · Cited by 10
Temporal Learning in Video Data Using Deep Learning and Gaussian Processes
Jha, D. K., Srivastav, A., & Ray, A. K. (2020) · International Journal of Prognostics and Health Management · doi:10.36001/ijphm.2016.v7i4.2460 · Cited by 10
Cartesian space robot manipulator clamping movement in ROS simulation and experiment
Mu, L., Zhou, Y., & Zhao, T. (2021) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns.2021.1.00011 · Cited by 10
COMPARATIVE ANALYSIS OF DEEP LEARNING ARCHITECTURES FOR GRAPE CLUSTER INSTANCE SEGMENTATION
Barbole, P. J. M. D. (2021) · INFORMATION TECHNOLOGY IN INDUSTRY · doi:10.17762/itii.v9i1.138 · Cited by 10
Revisiting Self-supervised Monocular Depth Estimation
Kim, U.-H., Lee, G.-M., & Kim, J.-H. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-97672-9_30 · Cited by 10
First‐break automatic picking technology based on semantic segmentation
Xu, Y., Yin, C., Pan, Y., Yudong, N., Xuefeng, Z., & Yang, T. (2021) · Geophysical Prospecting · doi:10.1111/1365-2478.13088 · Cited by 10
Tuberculosis Detection from CXR: An Approach Using Transfer Learning with Various CNN Architectures
Priya, P. A., & Vimina, E. R. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-33-4909-4_31 · Cited by 10
Learning From Noisy Labels via Dynamic Loss Thresholding
Yang, H., Jin, Y.-Z., Li, Z., Wang, D.-B., Geng, X., & Zhang, M.-L. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3313604 · Cited by 11
Portrait photography splicing detection using ensemble of convolutional neural networks
Revi, K. R., Wilscy, M., & Antony, R. (2021) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-189857 · Cited by 10
Acoustic Feature Analysis for Wet and Dry Road Surface Classification Using Two-stream CNN
Bahrami, S., Doraisamy, S. C., Azman, A., et al. (2020) · doi:10.1145/3445815.3445847 · Cited by 10
A Deep-Learning-Based Method for Diagnosing Time-Varying Plasma Adopting Microwaves
Liu, H., Yang, M., Liu, Y., Geng, J., & Tang, J. (2021) · IEEE Transactions on Plasma Science · doi:10.1109/tps.2021.3065343 · Cited by 10
Automatic Fruits Classification System Based on Deep Neural Network
Munir, K., Umar, A. I., & Yousaf, W. (2020) · NUST Journal of Engineering Sciences · doi:10.24949/njes.v13i1.501 · Cited by 10
Automatic, dynamic, and nearly optimal learning rate specification via local quadratic approximation
Zhu, Y., Huang, D., Gao, Y., Wu, R., Chen, Y., Zhang, B., & Wang, H. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.03.025 · Cited by 14
Diverse Features Fusion Network for video-based action recognition
Deng, H., Kong, J., Jiang, M., & Liu, T. (2021) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2021.103121 · Cited by 11
Waste Collection & Segregation using Computer Vision and Convolutional Neural Network for Vessels
Sruthy, V., Akshaya, Anjana, S., Ponnaganti, S. S., Pillai, V. G., & Preetha, P. K. (2021) · International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) · doi:10.1109/icccis51004.2021.9397092 · Cited by 10
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
