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 35,601–35,700 of 108,435
Wen, C., Bai, X., Yang, J., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-70311-y · Cited by 21
MelTrans: Mel-Spectrogram Relationship-Learning for Speech Emotion Recognition via Transformers
Li, H., Li, J., Liu, H., et al. (2024) · Sensors · doi:10.3390/s24175506 · Cited by 21
Moon, J., Suh, S., Jung, S., Baek, S., & Pyo, J. (2024) · GIScience & Remote Sensing · doi:10.1080/15481603.2024.2393489 · Cited by 22
IdeNet: Making Neural Network Identify Camouflaged Objects Like Creatures
Guan, J., Fang, X., Zhu, T., Cai, Z., Ling, Z., Yang, M., & Luo, J. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3449574 · Cited by 27
The Role of Artificial Intelligence and Machine Learning in Cardiovascular Imaging and Diagnosis
Reza-Soltani, S., Alam, L. F., Debellotte, O., Monga, T. S., Coyalkar, V. R., Tarnate, V. C. A., Ozoalor, C. U., Allam, S. R., Afzal, M., Shah, G. K., et al. (2024) · Cureus · doi:10.7759/cureus.68472 · Cited by 27
Heterogeneous graph social pooling for interaction-aware vehicle trajectory prediction
Mo, X., Xing, Y., & Lv, C. (2024) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2024.103748 · Cited by 24
Sun, L., Li, H., Nagel, J., & Yang, S. (2024) · Applied Sciences · doi:10.3390/app14177949 · Cited by 24
Scene Chinese Recognition with Local and Global Attention
Zhao, C., Yi, Y., Gan, C., Tang, Z., & Kong, D. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111013 · Cited by 23
Liu, X., Zhu, H., Song, W., Wang, J., Yan, L., & Wang, K. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3459928 · Cited by 21
Leite, D., Brito, A. V., & Faccioli, G. (2024) · Smart Agricultural Technology · doi:10.1016/j.atech.2024.100573 · Cited by 27
A multi-scale dense residual correlation network for remote sensing scene classification
Dai, W., Shi, F., Wang, X., Xu, H., Yuan, L., & Wen, X. (2024) · Scientific Reports · doi:10.1038/s41598-024-73252-8 · Cited by 29
Intention inference for space targets using deep convolutional neural network
Li, J., Yang, Z., & Luo, Y. (2024) · Advances in Space Research · doi:10.1016/j.asr.2024.10.006 · Cited by 22
Effectiveness of Generative AI for Post-Earthquake Damage Assessment
Estêvão, J. M. C. (2024) · Buildings · doi:10.3390/buildings14103255 · Cited by 28
A survey of deep learning techniques for detecting and recognizing objects in complex environments
Dogra, A. K., Sharma, V., & Sohal, H. (2024) · Computer Science Review · doi:10.1016/j.cosrev.2024.100686 · Cited by 25
Upadhyay, P., Tomar, P., & Yadav, S. P. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109796 · Cited by 21
Panwar, P., Chaurasia, S., Gangrade, J., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-78203-x · Cited by 21
Dhanya, V. G., Subeesh, A., Susmita, C., Amaresh, Saji, S. J., Dilsha, C., Keerthi, C., Nunavath, A., Singh, A., & Kumar, S. (2024) · Plant Physiology Reports · doi:10.1007/s40502-024-00839-8 · Cited by 22
Liu, J., & Guo, M. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.057660 · Cited by 21
Song, H., Zhou, X., Chen, C., Dong, C., He, Y., Wu, M., Yu, J., Chen, X., Li, Y., & Ma, B. (2024) · Scientific Reports · doi:10.1038/s41598-024-80590-0 · Cited by 20
Gasmi, K., Alyami, A., Hamid, O. H., Altaieb, M. O., Shahin, O. R., Ammar, L. B., Chouaib, H., & Shehab, A. (2024) · Diagnostics · doi:10.3390/diagnostics14242779 · Cited by 27
Steering Angle-Guided Multimodal Fusion Lane Detection for Autonomous Driving
Gong, Y., Zhang, X., Lu, J., Jiang, X., Wang, Z., Liu, H., Li, Z., Wang, L., Yang, Q., & Wu, X. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3507536 · Cited by 24
Yapay Zekâ: Alt Dalları ve Uygulama Alanları
İŞCAN, H., & Durgun, A. (2024) · Aksaray Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi · doi:10.52791/aksarayiibd.1574207 · Cited by 33
Bio-inspired neuron based on threshold selector and tunnel diode capable of excitability modulation
Ostrovskii, V. Y., Искандарович, К. Т., Rybin, V. G., Bobrova, Y. O., Arlyapov, V. A., & Бутусов, Д. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129454 · Cited by 23
Transformer-Driven Affective State Recognition from Wearable Physiological Data in Everyday Contexts
Li, F., & Zhang, D. (2025) · Sensors · doi:10.3390/s25030761 · Cited by 25
he, Z., Bi, H., Liang, B., Li, Z., Zhang, H., & Wang, Y. (2025) · Light Science & Applications · doi:10.1038/s41377-024-01713-w · Cited by 23
Dai, W., Fan, R., & Nan, J. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3550617 · Cited by 27
A toolbox for calculating quantitative image properties in aesthetics research
Redies, C., Bartho, R., Koßmann, L., Špehar, B., Hübner, R., Wagemans, J. P., & Hayn-Leichsenring, G. U. (2025) · Behavior Research Methods · doi:10.3758/s13428-025-02632-3 · Cited by 29
Unveiling the frontiers of deep learning: Innovations shaping diverse domains
Ahmed, S. F., Alam, M. S. B., Kabir, M., Afrin, S., Rafa, S. J., Mehjabin, A., & Gandomi, A. H. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06259-x · Cited by 30
Ferreira, F. R. T., do Couto, L. M., Domingues, G. D. M. B., & Saporetti, C. M. (2025) · Social Network Analysis and Mining · doi:10.1007/s13278-025-01408-z · Cited by 22
Explainability and vision foundation models: A survey
Kazmierczak, R., Berthier, É., Frehse, G., & Franchi, G. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103184 · Cited by 45
Emerging frontiers in protein structure prediction following the AlphaFold revolution
Rennie, M. L., & Oliver, M. R. (2025) · Journal of The Royal Society Interface · doi:10.1098/rsif.2024.0886 · Cited by 34
A Survey of Video Action Recognition Based on Deep Learning
Gong, P., & Luo, X. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113594 · Cited by 34
Bouanani, N. E., Laamrani, A., Hajji, H., Bourriz, M., Bourzeix, F., Abdelali, H. A., El-Battay, A., Amazirh, A., & Chehbouni, A. (2025) · Remote Sensing · doi:10.3390/rs17091597 · Cited by 23
Li, F., Luo, D., & Niu, D. (2025) · Communications Engineering · doi:10.1038/s44172-025-00431-4 · Cited by 29
Kabir, M. F., Ahmad, M. Y., Uddin, R., Cordero, M., & Kant, S. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00442-2 · Cited by 28
“The initial dataset comprised 131 images—87 labeled as cancerous and 44 as non-cancerous [18–20].”
A Review on Gas Pipeline Leak Detection: Acoustic-Based, OGI-Based, and Multimodal Fusion Methods
Gong, Y., Bao, C., He, Z., Jian, Y., Wang, X., Huang, H., & Song, X. (2025) · Information · doi:10.3390/info16090731 · Cited by 26
Embedded Artificial Intelligence: A Comprehensive Literature Review
Huang, X., Wang, H., Qin, S., & Tang, S.-K. (2025) · Electronics · doi:10.3390/electronics14173468 · Cited by 34
“Classic CNN architectures like LeNet-5 [99] laid the foundation for subsequent deeper and more complex networks (such as AlexNet [5], VGG [100], ResNet [101], and Inception [102]).”
Kamyshanska, H., & Memisevic, R. (2013) · Cited by 24
Distilled Collections from Textual Image Queries
Averbuch‐Elor, H., Wang, Y., Qian, Y., et al. (2015) · Computer Graphics Forum · doi:10.1111/cgf.12547 · Cited by 21
A Deep Learning Neural Network for Number Cognition: A bi-cultural study with the iCub
Nuovo, A. G. D., Cruz, V. M. D. L., & Cangelosi, A. (2015) · Joint IEEE International Conference on Development and Learning and Epigenetic Robotics · doi:10.1109/devlrn.2015.7346165 · Cited by 21
Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing
Izadinia, H., Sadeghi, F., Divvala, S., Hajishirzi, H., Choi, Y., & Farhadi, A. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.10 · Cited by 20
Box Aggregation for Proposal Decimation: Last Mile of Object Detection
Liu, S., Lu, C., & Jia, J. (2015) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.295 · Cited by 18
Pedestrian Detection via Mixture of CNN Experts and Thresholded Aggregated Channel Features
Verma, A., Hebbalaguppe, R., Vig, L., Kumar, S., & Hassan, E. (2015) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2015.78 · Cited by 21
Fine-Scale Surface Normal Estimation Using a Single NIR Image
Yoon, Y., Choe, G., Kim, N. I., Lee, J., & Kweon, I. S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9_30 · Cited by 19
DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers
Ghodrati, A., Diba, A., Pedersoli, M., Tuytelaars, T., & Gool, L. V. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1006-x · Cited by 21
Convolutional Neural Network for 3D object recognition using volumetric representation
Xu, X., Dehghani, A., Corrigan, D., Caulfield, S., & Moloney, D. (2016) · International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE) · doi:10.1109/splim.2016.7528403 · Cited by 21
Large-scale image retrieval with supervised sparse hashing
Xu, Y. L., Shen, F., Xu, X., Gao, L., Wang, Y., & Tan, X. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.05.109 · Cited by 21
Learning hierarchical video representation for action recognition
Li, Q., Qiu, Z., Yao, T., Mei, T., Rui, Y., & Luo, J. (2017) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-016-0117-4 · Cited by 20
Sequential Segment Networks for Action Recognition
Chen, Q.-Q., & Zhang, Y. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2689921 · Cited by 20
Robust Abdominal Organ Segmentation Using Regional Convolutional Neural Networks
Larsson, M., Zhang, Y., & Kahl, F. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59129-2_4 · Cited by 21
Scaling Deep Learning Workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing
Gawande, N. A., Landwehr, J., Daily, J. A., et al. (2017) · IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) · doi:10.1109/ipdpsw.2017.36 · Cited by 21
Deep Learning Models For Multiword Expression Identification
Gharbieh, W., Bhavsar, V. C., & Cook, P. F. (2017) · Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/s17-1006 · Cited by 21
Fundamentals of Texture Processing for Biomedical Image Analysis
Depeursinge, A., Fageot, J., & Al-Kadi, O. S. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-812133-7.00001-6 · Cited by 23
Weather-to-garment: Weather-oriented clothing recommendation
Liu, Y., Gao, Y., Feng, S., et al. (2017) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2017.8019476 · Cited by 21
Online Modeling of Esthetic Communities Using Deep Perception Graph Analytics
Zhang, L., Liu, M., Chen, L., Qiu, L., Zhang, C., Hu, Y., & Zimmermann, R. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2769799 · Cited by 21
Analytics of deep neural network in change detection
Minematsu, T., Shimada, A., & Taniguchi, R.-I. (2017) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2017.8078550 · Cited by 21
Player detection in field sports
Direkoğlu, C., Şah, M., & O'Connor, N. E. (2017) · Machine Vision and Applications · doi:10.1007/s00138-017-0893-8 · Cited by 21
Hou, Y., Wang, S., Wang, P., Gao, Z., & Li, W. (2017) · IEEE Access · doi:10.1109/access.2017.2782258 · Cited by 18
Qiao, K., Zeng, L., Chen, J., Hai, J., & Yan, B. (2018) · IET Image Processing · doi:10.1049/iet-ipr.2017.1208 · Cited by 21
Neural networks: Efficient implementations and applications
Zhang, C., & Xu, W. (2017) · IEEE International Conference on ASIC (ASICON) · doi:10.1109/asicon.2017.8252654 · Cited by 22
Soomro, M., Farooq, M. A., & Raza, R. H. (2017) · International Conference on Frontiers of Information Technology (FIT) · doi:10.1109/fit.2017.00071 · Cited by 23
Do citations and readership identify seminal publications?
Herrmannová, D., Patton, R. M., Knoth, P., & Stahl, C. G. (2018) · Scientometrics · doi:10.1007/s11192-018-2669-y · Cited by 26
Deep Spatial-Temporal Joint Feature Representation for Video Object Detection
Zhao, B., Zhao, B., Tang, L., Han, Y., & Wang, W. (2018) · Sensors · doi:10.3390/s18030774 · Cited by 24
Temporally Consistent Road Surface Profile Estimation Using Stereo Vision
Lee, J.-K., & Yoon, K. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2794342 · Cited by 21
A Case for Memory-Centric HPC System Architecture for Training Deep Neural Networks
Kwon, Y., & Rhu, M. (2018) · IEEE Computer Architecture Letters · doi:10.1109/lca.2018.2823302 · Cited by 21
An Improved Promoter Recognition Model Using Convolutional Neural Network
Qian, Y., Zhang, Y., Guo, B., Ye, S., Wu, Y., & Zhang, J. (2018) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2018.00072 · Cited by 22
A Large-Scale Evaluation Framework for EEG Deep Learning Architectures
Heilmeyer, F., Schirrmeister, R. T., Fiederer, L. D. J., Volker, M., Behncke, J., & Tonio Ball, A. (2018) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2018.00185 · Cited by 22
Are You Tampering with My Data?
Alberti, M., Pondenkandath, V., Würsch, M., Bouillon, M., Seuret, M., Ingold, R., & Liwicki, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11012-3_25 · Cited by 20
An efficient end-to-end deep learning architecture for activity classification
Mahjoub, A. B., & Atri, M. (2018) · Analog Integrated Circuits and Signal Processing · doi:10.1007/s10470-018-1306-2 · Cited by 21
Face Alignment With Expression- and Pose-Based Adaptive Initialization
Mo, H., Liu, L., Zhu, W., et al. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2867262 · Cited by 21
DeepDisc: Optic Disc Segmentation Based on Atrous Convolution and Spatial Pyramid Pooling
Gu, Z., Liu, P., Zhou, K. Q., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00949-6_30 · Cited by 21
Contrail Recognition with Convolutional Neural Network and Contrail Parameterizations Evaluation
Zhang, G., Zhang, J., & Shang, J. (2018) · SOLA · doi:10.2151/sola.2018-023 · Cited by 21
Interpretable Multimodal Deception Detection in Videos
Karimi, H. (2018) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3242969.3264967 · Cited by 22
Scene guided colorization using neural networks
Yu, X., Qu, S., & Wan, S. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3828-z · Cited by 21
Learning facial action units with spatiotemporal cues and multi-label sampling
Chu, W.-S., la Torre, F. D., & Cohn, J. F. (2018) · Image and Vision Computing · doi:10.1016/j.imavis.2018.10.002 · Cited by 21
Integrating Domain Knowledge: Using Hierarchies to Improve Deep Classifiers
Brust, C.-A., & Denzler, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-41404-7_1 · Cited by 27
Binary Sketches for Secondary Filtering
Míč, V., Novák, D., & Zezula, P. (2018) · ACM Transactions on Information Systems · doi:10.1145/3231936 · Cited by 21
Classification of Eye Tracking Data Using a Convolutional Neural Network
Yin, Y., Juan, C., Chakraborty, J., & McGuire, M. P. (2018) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2018.00085 · Cited by 22
Underwater Place Recognition in Unknown Environments with Triplet Based Acoustic Image Retrieval
Ribeiro, P. O. C. S., dos Santos, M. M., Drews, P., Botelho, S. S. D. C., Longaray, L. M., Giacomo, G. G. D., & Pias, M. R. (2018) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2018.00084 · Cited by 21
Effect of Adversarial Examples on the Robustness of CAPTCHA
Zhang, Y., Gao, H., Pei, G., Kang, S., & Zhou, X. (2018) · International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) · doi:10.1109/cyberc.2018.00013 · Cited by 22
Exploiting Mid-Level Semantics for Large-Scale Complex Video Classification
Zhang, J., Mei, K., Zheng, Y., & Jianping Fan, B. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2907453 · Cited by 22
Patel, J. S., Fioranelli, F., Ritchie, M., & Griffiths, H. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2909685 · Cited by 21
ARA: Cross-Layer approximate computing framework based reconfigurable architecture for CNNs
Gong, Y., Liu, B., Ge, W., & Shi, L. (2019) · Microelectronics Journal · doi:10.1016/j.mejo.2019.03.011 · Cited by 21
Bonsai: diverse and shallow trees for extreme multi-label classification
Khandagale, S., Xiao, H., & Babbar, R. (2020) · Machine Learning · doi:10.1007/s10994-020-05888-2 · Cited by 138
3D Hyper-Dense Connected Convolutional Neural Network for Brain Tumor Segmentation
Qamar, S., Jin, H., Zheng, R., & Ahmad, P. (2018) · International Conference on Semantics, Knowledge and Grid (SKG) · doi:10.1109/skg.2018.00024 · Cited by 21
Multi-person 3D pose estimation from 3D cloud data using 3D convolutional neural networks
Vasileiadis, M., Bouganis, C.-S., & Tzovaras, D. K. (2019) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.04.011 · Cited by 21
Lin, C.-H., Lin, C.-H., Wang, S.-H., et al. (2019) · Applied Intelligence · doi:10.1007/s10489-019-01486-5 · Cited by 21
Multi-Person Tracking Based on Faster R-CNN and Deep Appearance Features
Khan, G., Tariq, Z., & Khan, M. U. G. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.85215 · Cited by 22
Zhong, J., & Li, W. H. C. (2019) · International Conference on Innovation in Artificial Intelligence (ICIAI) · doi:10.1145/3319921.3319937 · Cited by 21
Deep convolutional neural networks for land-cover classification with Sentinel-2 images
Kroupi, E., Kesa, M., Navarro-Sanchez, V. D., Saeed, S., Pelloquin, C., Alhaddad, B., Moreno, L., Soria‐Frisch, A., & Ruffini, G. (2019) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.13.024525 · Cited by 21
Scalable Multi Corpora Neural Language Models for ASR
Raju, A., Filimonov, D., Tiwari, G., Lan, G., & Rastrow, A. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-3060 · Cited by 28
Artificial Intelligence in Skin Cancer
Reiter, O., Rotemberg, V. M., Köse, K., & Halpern, A. C. (2019) · Current Dermatology Reports · doi:10.1007/s13671-019-00267-0 · Cited by 21
A Framework for Self-Training Perceptual Agents in Simulated Photorealistic Environments
Mania, P., & Beetz, M. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793474 · Cited by 23
Real-Time Robotic Grasping and Localization Using Deep Learning-Based Object Detection Technique
Farag, M., Ghafar, A., & Alsibai, M. H. (2019) · IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) · doi:10.1109/i2cacis.2019.8825093 · Cited by 26
Multi-scale Spatially-Asymmetric Recalibration for Image Classification
Wang, Y., Xie, L., Qiao, S., Zhang, Y., Zhang, W., & Yuille, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01261-8_31 · Cited by 19
Image steganalysis based on convolutional neural network and feature selection
Sun, Z., Li, F., Huang, H., et al. (2019) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.5469 · Cited by 21
MAT-Net: Medial Axis Transform Network for 3D Object Recognition
Hu, J., Wang, B., Qian, L., et al. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/109 · Cited by 21
Park, K.-M., Shin, D.-H., & Chi, S.-D. (2019) · Applied Sciences · doi:10.3390/app9153176 · Cited by 21
A Lightweight Neural Network Based on AlexNet-SSD Model for Garbage Detection
Lee, S., Yeh, C.-H., Hou, T., et al. (2019) · doi:10.1145/3341069.3341087 · Cited by 21
PeTaL (Periodic Table of Life) and Physiomimetics
Shyam, V., Friend, L., Whiteaker, B., Bense, N. B., Dowdall, J. B., Boktor, B., Johny, M., Reyes, I., Naser, A., Sakhamuri, N., et al. (2019) · Designs · doi:10.3390/designs3030043 · Cited by 21
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
