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 32,201–32,300 of 108,435
Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance
Pham, M., Mercier, G., & Bombrun, L. (2017) · Journal of Imaging · doi:10.3390/jimaging3040043 · Cited by 25
Deep Multibranch Neural Network for Painting Categorization
Bianco, S., Mazzini, D., & Schettini, R. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68560-1_37 · Cited by 25
Pose recognition using convolutional neural networks on omni-directional images
Georgakopoulos, S. V., Kottari, K., Delibasis, K. K., Plagianakos, V. P., & Maglogiannis, I. G. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.08.071 · Cited by 25
Yao, Y., Jiang, Z., Zhang, H., et al. (2017) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2017.8127710 · Cited by 25
Residual squeeze CNDS deep learning CNN model for very large scale places image recognition
Verma, A., Qassim, H., & Feinzimer, D. (2017) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon.2017.8248975 · Cited by 28
Implementation of deep-learning based image classification on single board computer
Shiddieqy, H. A., Hariadi, F. I., & Adiono, T. (2017) · International Symposium on Electronics and Smart Devices (ISESD) · doi:10.1109/isesd.2017.8253319 · Cited by 25
Vehicle re-identification by fusing multiple deep neural networks
Cui, C., Sang, N., Gao, C., & Zou, L. (2017) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2017.8310090 · Cited by 25
An automatic aperture detection system for LED cup based on machine vision
Yang, Y., Lou, Y., Gao, M., et al. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-5639-8 · Cited by 25
A Multi-Modality Deep Network for Cold-Start Recommendation
Sun, M., Li, F., & Zhang, J. (2018) · Big Data and Cognitive Computing · doi:10.3390/bdcc2010007 · Cited by 26
Research on weeds identification based on K-means feature learning
Tang, J., Zhang, Z., Wang, D., Xin, J. L., & He, L. (2018) · Soft Computing · doi:10.1007/s00500-018-3125-x · Cited by 25
Broadcasting Convolutional Network for Visual Relational Reasoning
Chang, S., Yang, J., Park, S., & Kwak, N. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01267-0_46 · Cited by 22
Large-Scale Image Retrieval with Elasticsearch
Amato, G. M., Bolettieri, P., Carrara, F., et al. (2018) · doi:10.1145/3209978.3210089 · Cited by 25
Crowd Abnormal Detection Using Two-Stream Fully Convolutional Neural Networks
Wei, H., Xiao, Y., Li, R., et al. (2018) · International Conference on Measuring Technology and Mechatronics Automation (ICMTMA) · doi:10.1109/icmtma.2018.00087 · Cited by 25
Tong, M., Chen, Y., Zhao, M., et al. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3554-6 · Cited by 25
Image spam filtering using convolutional neural networks
Aiwan, F., & Zhaofeng, Y. (2018) · Personal and Ubiquitous Computing · doi:10.1007/s00779-018-1168-8 · Cited by 25
A Co-Point Mapping-Based Approach to Drivable Area Detection for Self-Driving Cars
Liu, Z., Yu, S., & Zheng, N. (2018) · Engineering · doi:10.1016/j.eng.2018.07.010 · Cited by 25
A cyclic self-learning Chinese word segmentation for the geoscience domain
Qiu, Q., Xie, Z., & Wu, L. (2018) · Geomatica · doi:10.1139/geomat-2018-0007 · Cited by 27
Automatic detection of motion artifacts on MRI using Deep CNN
Fantini, I., Rittner, L., Yasuda, C. L., & Lotufo, R. (2018) · International Workshop on Pattern Recognition in NeuroImaging · doi:10.1109/prni.2018.8423948 · Cited by 30
Deep Learning for Military Image Captioning
Das, S., Jain, L., & Das, A. (2018) · International Conference on Information Fusion (FUSION) · doi:10.23919/icif.2018.8455321 · Cited by 33
Discriminative Deep Feature Learning for Semantic-Based Image Retrieval
Song, K., Li, F., Long, F., Wang, J., & Ling, Q. (2018) · IEEE Access · doi:10.1109/access.2018.2862464 · Cited by 26
Image Dehazing for Object Recognition using Faster RCNN
Nalla, B. T., Sharma, T., Verma, N. K., et al. (2018) · doi:10.1109/ijcnn.2018.8489280 · Cited by 25
Utilizing Mobile-based Deep Learning Model for Managing Video in Knowledge Management System
Prabowo, H., Cenggoro, T. W., Budiarto, A., Perbangsa, A. S., Muljo, H. H., & Pardamean, B. (2018) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v12i6.8563 · Cited by 27
Deng, Y., Sander, A., Faulstich, L. C., & Denecke, K. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.10.001 · Cited by 24
Three Applications of Deep Learning Algorithms for Object Detection in Satellite Imagery
Napiórkowska, M., Petit, D., & Martí, P. (2018) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2018.8518102 · Cited by 25
On Hallucinating Context and Background Pixels from a Face Mask using Multi-scale GANs
Banerjee, S., Scheirer, W. J., Bowyer, K. W., & Flynn, P. J. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093568 · Cited by 26
Combustion Regime Monitoring by Flame Imaging and Machine Learning
Abdurakipov, S. S., Gobyzov, O. A., Tokarev, M. P., et al. (2018) · Optoelectronics Instrumentation and Data Processing · doi:10.3103/s875669901805014x · Cited by 25
Automatic Facial Attractiveness Prediction by Deep Multi-Task Learning
Gao, L., Li, W., Huang, Z., et al. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545033 · Cited by 25
Deep Learning based Human Activity Classification in Radar Micro-Doppler Image
He, Y., Yang, Y., Lang, Y., Huang, D., Jing, X., & Hou, C. (2018) · European Radar Conference (EuRAD) · doi:10.23919/eurad.2018.8546615 · Cited by 27
Traffic Speed Estimation from Surveillance Video Data: For the 2nd NVIDIA AI City Challenge Track 1
Huang, T. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00029 · Cited by 26
Deep Neural Networks: A Comparison on Different Computing Platforms
Modasshir, M., Li, A. Q., & Rekleitis, I. M. (2018) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2018.00060 · Cited by 26
A Deep Feature Extraction Method for HEp-2 Cell Image Classification
Vununu, C., Lee, S., & Kwon, K. (2018) · Electronics · doi:10.3390/electronics8010020 · Cited by 25
Implementation of Victims Detection Framework on Post Disaster Scenario
Sulistijono, I. A., Imansyah, T., Muhajir, M., Sutoyo, E., Anwar, M. K., Satriyanto, E., Basuki, A., & Risnumawan, A. (2018) · International Electronics Symposium (IES) · doi:10.1109/elecsym.2018.8615503 · Cited by 27
Vision-Based Obstacle Removal System for Autonomous Ground Vehicles Using a Robotic Arm
Asadi, K., Jain, R., Qin, Z., et al. (2019) · ASCE International Conference on Computing in Civil Engineering (i3CE) · doi:10.1061/9780784482438.042 · Cited by 25
Information System Design for Deep Learning Based Plant Counting Automation
Cenggoro, T. W., Budiarto, A., Rahutomo, R., & Pardamean, B. (2018) · Indonesian Association for Pattern Recognition International Conference (INAPR) · doi:10.1109/inapr.2018.8627019 · Cited by 29
CMOS Implementations of Rectified Linear Activation Function
Priyanka, P., Nisarga, G. K., & Raghuram, S. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-5950-7_11 · Cited by 25
An Approach for Validating Quality of Datasets for Machine Learning
Ding, J., & Li, X. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622640 · Cited by 25
Zhang, M., Liu, S., Yang, F., & Liu, J. (2019) · IEEE Access · doi:10.1109/access.2019.2900327 · Cited by 25
Distinguishing mirror from glass: A “big data” approach to material perception
Tamura, H., Prokott, K. E., & Fleming, R. W. (2022) · Journal of Vision · doi:10.1167/jov.22.4.4 · Cited by 24
EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching From Scratch
Ren, J. C., Li, Z., Yang, J., et al. (2019) · 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/cvpr.2019.00927 · Cited by 25
Predicting Time-to-Failure of Plasma Etching Equipment using Machine Learning
Jalali, A., Heistracher, C., Schindler, A., Haslhofer, B., Nemeth, T., Glawar, R., Sihn, W., & Boer, P. D. (2019) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm.2019.8819404 · Cited by 32
Theóphilo, A., Pereira, L. A. M., & Rocha, A. D. R. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683747 · Cited by 25
Multi-attention Network for Thoracic Disease Classification and Localization
Ma, Y., Zhou, Q., Chen, X., et al. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682952 · Cited by 25
An Improved Deep Neural Network for Classification of Plant Seedling Images
Alimboyong, C. R., & Hernandez, A. A. (2019) · IEEE International Colloquium on Signal Processing & Its Applications (CSPA) · doi:10.1109/cspa.2019.8696009 · Cited by 27
Regression-Based Three-Dimensional Pose Estimation for Texture-Less Objects
Liu, Y., Zhou, L., Zong, H., Gong, X., Wu, Q., Liang, Q., & Wang, J. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2913321 · Cited by 25
Convolutional Neural Network with SVM for Classification of Animal Images
Manohar, N., Kumar, Y. H. S., Rani, R., et al. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-981-13-5802-9_48 · Cited by 25
Detecting Adversarial Perturbations with Saliency
Zhang, C., Ye, Z., Wang, Y., et al. (2018) · IEEE International Conference on Signal and Image Processing (ICSIP) · doi:10.1109/siprocess.2018.8600516 · Cited by 25
Denoising Convolutional Autoencoder Based B-mode Ultrasound Tongue Image Feature Extraction
Li, B., Xu, K., Feng, D., Mi, H., Wang, H., & Zhu, J. Y. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682806 · Cited by 31
Adaptive Deep Reuse: Accelerating CNN Training on the Fly
Ning, L., Guan, H., & Shen, X. (2019) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2019.00138 · Cited by 25
Wani, M. A., Bhat, F. A., Afzal, S., & Khan, A. I. (2019) · Studies in big data · doi:10.1007/978-981-13-6794-6_1 · Cited by 28
Automatic Extraction of Gravity Waves from All-Sky Airglow Image Based on Machine Learning
Lai, C., Xu, J., Yue, J., Yuan, W., Liu, X., Li, W., & Li, Q. (2019) · Remote Sensing · doi:10.3390/rs11131516 · Cited by 26
Ghodrati, S., Sharma, H., Kinzer, S., Yazdanbakhsh, A., Park, J., Kim, N. S., Burger, D., & Esmaeilzadeh, H. (2020) · International Conference on Parallel Architectures and Compilation Techniques (PACT) · doi:10.1145/3410463.3414634 · Cited by 26
Kim, T. K., Park, J. K., Lee, B. H., & Seong, S.-H. (2019) · Annals of Nuclear Energy · doi:10.1016/j.anucene.2019.07.022 · Cited by 25
Capsule Networks for Object Detection in UAV Imagery
Mekhalfi, M. L., Bejiga, M. B., Soresina, D., Melgani, F., & Demir, B. (2019) · Remote Sensing · doi:10.3390/rs11141694 · Cited by 26
Fast dynamic routing based on weighted kernel density estimation
Zhang, S., Zhao, W., Wu, X., & Zhou, Q. (2019) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.5281 · Cited by 25
First Experiments with Neural Translation of Informal to Formal Mathematics
Wang, Q., Kaliszyk, C., & Urban, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-96812-4_22 · Cited by 30
Identifying carrot appearance quality by transfer learning
Hongfei, Z., Deng, L., Wang, D., Gao, J., Ni, J., & Han, Z. (2019) · Journal of Food Process Engineering · doi:10.1111/jfpe.13187 · Cited by 26
Hierarchical Spatial Sum–Product Networks for Action Recognition in Still Images
Wang, J., & Wang, G. (2016) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2016.2586853 · Cited by 25
Color-Sensitive Person Re-Identification
Wang, G., Yang, Y., Cheng, J., Wang, J., & Hou, Z. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/131 · Cited by 24
Convolutional Gaussian Embeddings for Personalized Recommendation with Uncertainty
Jiang, J., Yang, D., Xiao, Y., & Shen, C. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/367 · Cited by 33
Zheng, Y., Kong, J., Jin, X., Wang, X., Su, T., & Wang, J.-L. (2019) · IEEE Access · doi:10.1109/access.2019.2933169 · Cited by 25
Chen, Y., Chiang, S.-W., & Wu, M.-L. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02516-x · Cited by 28
Multiscale spatial gradient features for 18F-FDG PET image-guided diagnosis of Alzheimer’s disease
Pan, X., Adel, M., Fossati, C., Gaidon, T., Wojak, J., & Guedj, É. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105027 · Cited by 24
Physics informed neural networks for velocity inversion
Xu, Y., Li, J., & Chen, X. (2019) · Second International Meeting for Applied Geoscience & Energy · doi:10.1190/segam2019-3216823.1 · Cited by 25
Itzkovich, D., Sharon, Y., Jarc, A., Refaely, Y., & Nisky, I. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793963 · Cited by 23
Deep learning the semantics of change sequences for query expansion
Huang, Q. Y., Yang, Y., & Cheng, M. (2019) · Software Practice and Experience · doi:10.1002/spe.2736 · Cited by 25
Image Pre-Transformation for Recognition-Aware Image Compression
Suzuki, S., Takagi, M., Hayase, K., Onishi, T., & Shimizu, A. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803275 · Cited by 26
Multi-Component Fusion Network for Small Object Detection in Remote Sensing Images
Liu, J., Yang, S., Tian, L., Guo, W., Zhou, B., Jia, J., & Ling, H. (2019) · IEEE Access · doi:10.1109/access.2019.2939488 · Cited by 25
Predicting the future direction of cell movement with convolutional neural networks
Nishimoto, S., Tokuoka, Y., Yamada, T., Hiroi, N. F., & Funahashi, A. (2019) · PLoS ONE · doi:10.1371/journal.pone.0221245 · Cited by 27
Secure multiparty learning from the aggregation of locally trained models
Ma, X., Ji, C., Zhang, X., Wang, J., Li, J., Li, K., & Chen, X. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102754 · Cited by 25
A Combined Deep Learning Model for the Scene Classification of High-Resolution Remote Sensing Image
Dong, Y., & Zhang, Q. (2019) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2019.2902675 · Cited by 25
Embedding topological features into convolutional neural network salient object detection
Zhou, L., & Gu, X. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.09.009 · Cited by 25
Zhao, S., Wang, E., & Hao, J. (2019) · Journal of Vibroengineering · doi:10.21595/jve.2019.20781 · Cited by 25
Emotion Recognition from 2D Facial Expressions
Taha, B., & Hatzinakos, D. (2019) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece.2019.8861751 · Cited by 25
The Importance of Imaging Informatics and Informaticists in the Implementation of AI
Cook, T. S. (2019) · Academic Radiology · doi:10.1016/j.acra.2019.10.002 · Cited by 24
Kim, M.-G. (2019) · International Journal of Precision Engineering and Manufacturing · doi:10.1007/s12541-019-00260-4 · Cited by 25
Optical interconnection networks for high-performance systems
Cheng, Q., Glick, M. S., & Bergman, K. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816502-7.00020-8 · Cited by 24
A Weakly Supervised Fine Label Classifier Enhanced by Coarse Supervision
Taherkhani, F., Kazemi, H., Dabouei, A., Dawson, J. M., & Nasrabadi, N. M. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00656 · Cited by 24
A Dual Neural Architecture Combined SqueezeNet with OctConv for LiDAR Data Classification
Wang, A., Wang, M., Jiang, K., Cao, M., & Iwahori, Y. (2019) · Sensors · doi:10.3390/s19224927 · Cited by 26
Costa, M. G. F., Campos, J. P. M., Aquino, G., Pereira, W. C. D. A., & Filho, C. F. F. C. (2019) · BMC Medical Imaging · doi:10.1186/s12880-019-0389-2 · Cited by 25
Zhu, R., Tu, X., & Huang, J. X. (2019) · Studies in big data · doi:10.1007/978-3-030-33966-1_13 · Cited by 29
ConvNets-based action recognition from skeleton motion maps
Chen, Y., Wang, L., Li, C., Hou, Y., & Li, W. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08261-1 · Cited by 24
Going Deeper With Lean Point Networks
Lê, E.-T., Kokkinos, I., & Mitra, N. J. (2020) · 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/cvpr42600.2020.00952 · Cited by 26
POD: Practical Object Detection With Scale-Sensitive Network
Peng, J., Sun, M., Zhang, Z., Tan, T., & Yan, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00970 · Cited by 26
Champigny, M. J., Unda, F., Skyba, O., Soolanayakanahally, R. Y., Mansfield, S. D., & Campbell, M. M. (2019) · Plant Biotechnology Journal · doi:10.1111/pbi.13299 · Cited by 26
Thakuriah, P. V., Siła-Nowicka, K., Hong, J., Boididou, C., Osborne, M., Lido, C. M., & McHugh, A. (2019) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2019.101427 · Cited by 27
What is a Tabby? Interpretable Model Decisions by Learning Attribute-Based Classification Criteria
Liu, H., Wang, R., Shan, S., & Chen, X. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2954501 · Cited by 25
Vision-based MAV Navigation in Underground Mine Using Convolutional Neural Network
Mansouri, S. S., Karvelis, P. S., Kanellakis, C., et al. (2019) · Proceedings of the Annual Conference of the IEEE Industrial Electronics Society · doi:10.1109/iecon.2019.8927168 · Cited by 25
Binarized Neural Architecture Search
Chen, H., Zhuo, L., Zhang, B., Zheng, X., Liu, J., Doermann, D., & Ji, R. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6624 · Cited by 27
Wang, Y., Li, Y., Song, Y. H., & Rong, X. (2019) · IEEE Access · doi:10.1109/access.2019.2961161 · Cited by 24
NAIS: Neural Architecture and Implementation Search and its Applications in Autonomous Driving
Hao, C., Chen, Y., Liu, X., Sarwari, A., Sew, D., Dhar, A., Wu, B., Fu, D., Xiong, J., Hwu, W., et al. (2019) · doi:10.1109/iccad45719.2019.8942055 · Cited by 25
Ultra Fine-Grained Image Semantic Embedding
Juan, D.-C., Lu, C.-T., Li, Z., Peng, F., Timofeev, A., Chen, Y.-T., Gao, Y., Duerig, T., Tomkins, A., & Ravi, S. (2020) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3336191.3371784 · Cited by 25
Learning Deep Neural Network Controllers for Dynamical Systems with Safety Guarantees: Invited Paper
Deshmukh, J. V., Kapinski, J. P., Yamaguchi, T., & Prokhorov, D. V. (2019) · doi:10.1109/iccad45719.2019.8942130 · Cited by 26
Clustering Facial Attributes: Narrowing the Path From Soft to Hard Biometrics
Abate, A. F., Barra, P., Barra, S., Molinari, C., Nappi, M., & Narducci, F. (2019) · IEEE Access · doi:10.1109/access.2019.2962010 · Cited by 26
Surface Classification of Damaged Concrete Using Deep Convolutional Neural Network
Hung, P. D., Su, N. T., & Diep, V. T. (2019) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661819040047 · Cited by 25
No-reference video quality evaluation by a deep transfer CNN architecture
Hou, R., Zhao, Y., Hu, Y., & Liu, H. (2020) · Signal Processing Image Communication · doi:10.1016/j.image.2020.115782 · Cited by 25
A Comparison of Quantized Convolutional and LSTM Recurrent Neural Network Models Using MNIST
Kaziha, O., & Bonny, T. (2019) · International Conference on Electrical and Computing Technologies and Applications (ICECTA) · doi:10.1109/icecta48151.2019.8959793 · Cited by 26
Dilated Capsule Network for Brain Tumor Type Classification Via MRI Segmented Tumor Region
Adu, K., Yu, Y., Cai, J., & Tashi, N. (2019) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio49542.2019.8961610 · Cited by 26
Blood pressure prediction from speech recordings
Ankışhan, H. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101842 · Cited by 25
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results
Fuoli, D., Huang, Z., Gu, S., Timofte, R., Raventós, A., & Guo, T. (2020) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · doi:10.3929/ethz-b-000458821 · Cited by 21
Jabbar, R., Shinoy, M., Kharbeche, M. B. M., et al. (2020) · IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) · doi:10.1109/iciot48696.2020.9089484 · Cited by 25
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
