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 50,901–51,000 of 108,439
AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning
Albrecht, C. M., Marianno, F. J., & Klein, L. J. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9672060 · Cited by 16
Sharma, R., Kaushik, B., Gondhi, N. K., Tahir, M., & Rahmani, M. K. I. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.024232 · Cited by 13
Diabetic Retinopathy Diagnosis Using ResNet with Fuzzy Rough C-Means Clustering
Rajkumar, R. S., & Selvarani, A. G. (2022) · Computer Systems Science and Engineering · doi:10.32604/csse.2022.021909 · Cited by 11
Automated Grooming Detection of Mouse by Three-Dimensional Convolutional Neural Network
Sakamoto, N., Kobayashi, K., Yamamoto, T., Masuko, S., Yamamoto, M., & Murata, T. (2022) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2022.797860 · Cited by 11
SECURE-GEGELATI Always-On Intrusion Detection through GEGELATI Lightweight Tangled Program Graphs
Sourbier, N., Desnos, K., Guyet, T., Majorczyk, F., Gesny, O., & Pelcat, M. (2022) · Journal of Signal Processing Systems · doi:10.1007/s11265-021-01728-1 · Cited by 10
Age Prediction using Image Dataset using Machine Learning
(2020) · International Journal of Innovative Technology and Exploring Engineering · doi:10.35940/ijitee.l1020.10812s319 · Cited by 11
An empirical study of cyclical learning rate on neural machine translation
Wang, W., Lee, C. M., Liu, J., Çolakoğlu, T., & Peng, W. (2022) · Natural Language Engineering · doi:10.1017/s135132492200002x · Cited by 11
Multi Label Classification Of Retinal Disease On Fundus Images Using AlexNet And VGG16 Architectures
Prawira, R., Bustamam, A., & Anki, P. (2021) · International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) · doi:10.1109/isriti54043.2021.9702817 · Cited by 11
Chharia, A., Upadhyay, R., Kumar, V., Cheng, C., Zhang, J., Wang, T., & Xu, M. (2022) · IEEE Access · doi:10.1109/access.2022.3153059 · Cited by 11
Self-Guidance: Improve Deep Neural Network Generalization via Knowledge Distillation
Zheng, Z., & Peng, X. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00351 · Cited by 10
Borrow from Source Models: Efficient Infrared Object Detection with Limited Examples
Chen, R., Liu, S., Mu, J., Miao, Z., & Li, F. (2022) · Applied Sciences · doi:10.3390/app12041896 · Cited by 11
Detection of tyre defects using weighted quality-based convolutional neural network
Rajeswari, M., Julie, E. G., Robinson, Y. H., Joseph, E., Arun, A., Sebastian, E., Kumar, R., Long, H. V., & Sơn, L. H. (2022) · Soft Computing · doi:10.1007/s00500-022-06878-3 · Cited by 11
Khosla, M., & Wehbe, L. (2026) · Nature Communications · doi:10.1038/s41467-026-73938-9 · Cited by 11
Double Mask R-CNN for Pedestrian Detection in a Crowd
Liu, C., Wang, H., & Liu, C. (2022) · Mobile Information Systems · doi:10.1155/2022/4012252 · Cited by 13
Tang, Z., Chen, Y., Hu, R., & Wang, H. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103656 · Cited by 11
Convolutional Neural Networks for Mechanistic Driver Detection in Atrial Fibrillation
Ríos-Muñoz, G. R., Fernández‐Avilés, F., & Arenal, Á. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23084216 · Cited by 11
DistractNet: a deep convolutional neural network architecture for distracted driver classification
Nasri, I., Karrouchi, M., Snoussi, H., Kassmi, K., & Messaoudi, A. (2022) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v11.i2.pp494-503 · Cited by 13
CYTOMEGALOVIRUS RETINITIS SCREENING USING MACHINE LEARNING TECHNOLOGY
Srisuriyajan, P., Cheewaruangroj, N., Polpinit, P., et al. (2022) · Retina · doi:10.1097/iae.0000000000003506 · Cited by 11
Assessment of Building Damage on Post-Hurricane Satellite Imagery using improved CNN
Ishraq, A., Lima, A. A., Kabir, M. M., Rahman, M. S., & Mridha, M. F. (2022) · International Conference on Decision Aid Sciences and Applications (DASA) · doi:10.1109/dasa54658.2022.9765025 · Cited by 13
Improved vehicle detection systems with double-layer LSTM modules
Yang, W., Liow, W.-J., Chen, S.-F., Yang, J., Chung, P., & Mao, S. (2022) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-022-00839-6 · Cited by 12
Latent Space Explanation by Intervention
Gat, I., Lorberbom, G., Schwartz, I., & Hazan, T. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19948 · Cited by 14
Human and machine collaboration for painting game assets with deep learning
Serpa, Y. R., & Rodriguês, M. A. F. (2022) · Entertainment Computing · doi:10.1016/j.entcom.2022.100497 · Cited by 13
Predicting Tongue Motion in Unlabeled Ultrasound Video Using 3D Convolutional Neural Networks
Wu, C., Chen, S., Sheng, G., et al. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461957 · Cited by 11
A New Dataset and Performance Evaluation of a Region-based CNN for Urban Object Detection
Dominguez-Sanchez, A., Orts‐Escolano, S., García‐Rodríguez, J., & Cazorla, M. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489478 · Cited by 11
Research status and applications of nature-inspired algorithms for agri-food production
Huang, Y. (2020) · International journal of agricultural and biological engineering · doi:10.25165/j.ijabe.20201304.5501 · Cited by 11
Muñoz, N. L., Valero, A., Tejero, R. G., & Zoni, D. (2022) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2022.102553 · Cited by 11
Deep Learning Models for COVID-19 Detection
Serte, S., Dirik, M. A., & Al-Turjman, F. (2022) · Sustainability · doi:10.3390/su14105820 · Cited by 13
Pair-Wise Similarity Knowledge Distillation for RSI Scene Classification
Zhao, H., Sun, X., Gao, F., et al. (2022) · Remote Sensing · doi:10.3390/rs14102483 · Cited by 11
Convolutional Neural Network dalam Citra Medis
Gunawan, D., & Setiawan, H. (2022) · KONSTELASI Konvergensi Teknologi dan Sistem Informasi · doi:10.24002/konstelasi.v2i2.5367 · Cited by 36
Few-shot personalized saliency prediction using meta-learning
Luo, X., Liu, Z., Wei, W., et al. (2022) · Image and Vision Computing · doi:10.1016/j.imavis.2022.104491 · Cited by 11
Minowa, Y., Kubota, Y., & Nakatsukasa, S. (2022) · Forests · doi:10.3390/f13060943 · Cited by 11
AutoByte: Automatic Configuration for Optimal Communication Scheduling in DNN Training
Ma, Y., Wang, H. H., Zhang, Y., et al. (2022) · doi:10.1109/infocom48880.2022.9796752 · Cited by 11
A structural characterization of shortcut features for prediction
Bellamy, D., Hernán, M. Á., & Beam, A. L. (2022) · European Journal of Epidemiology · doi:10.1007/s10654-022-00892-3 · Cited by 13
Zhang, P., Li, Z., Zhang, H., Ding, J., Zhang, X., Peng, R., & Feng, Y. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.102901 · Cited by 12
NrtNet: An Unsupervised Method for 3D Non-Rigid Point Cloud Registration Based on Transformer
Hu, X., Zhang, D., Chen, J., Wu, Y., & Chen, Y. (2022) · Sensors · doi:10.3390/s22145128 · Cited by 11
Lu, M., Liu, J., Wang, F., & Xiang, Y. (2022) · Remote Sensing · doi:10.3390/rs14143450 · Cited by 14
Wang, Q., Zhang, K., Zhao, H., Zhang, H., Zhang, L., Yan, X., Liu, P., Fan, L., Yang, Y., & Yao, J. (2022) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2022.110931 · Cited by 12
Xie, W., Jiao, L., & Hua, W. (2022) · Remote Sensing · doi:10.3390/rs14153737 · Cited by 11
Recovery of impenetrable rough surface profiles via CNN-based deep learning architecture
Aydin, İ., Budak, G., Sefer, A., & Yapar, A. (2022) · International Journal of Remote Sensing · doi:10.1080/01431161.2022.2105177 · Cited by 12
Research on identification method of tangerine peel year based on deep learning
Chu, Z., Li, F., Wang, D., Xu, S., Chunfeng, G. A. O., & Bai, H. (2022) · Food Science and Technology · doi:10.1590/fst.64722 · Cited by 11
Hand Gesture and Human-Drone Interaction
Latif, B., Buckley, N., & Secco, E. L. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-16075-2_20 · Cited by 11
Uchiyama, R., Okada, Y., Kakizaki, R., & Tomioka, S. (2022) · Bioengineering · doi:10.3390/bioengineering9090430 · Cited by 11
SSoB: searching a scene-oriented architecture for underwater object detection
Yuan, W., Fu, C., Liu, R., & Fan, X. (2022) · The Visual Computer · doi:10.1007/s00371-022-02654-4 · Cited by 11
Modified CNN Architecture for Efficient Classification of Glioma Brain Tumour
Jeba, J. A., Devi, S. N., & Meena, M. (2022) · IETE Journal of Research · doi:10.1080/03772063.2022.2101553 · Cited by 11
Mowen, D., Munian, Y., & Alamaniotis, M. (2022) · Sustainability · doi:10.3390/su141912133 · Cited by 15
Forest based on Interval Transformation (FIT): A time series classifier with adaptive features
Li, G., Xu, S., Wang, S., & Yu, P. S. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118923 · Cited by 11
Zhang, F., Xu, Y., Zhou, Z., Zhang, H., & Yang, K. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106182 · Cited by 11
An Efficient Supervised Deep Hashing Method for Image Retrieval
Hussain, A., Li, H.-C., Ali, M., Wali, S., Hussain, M., & Rehman, A. (2022) · Entropy · doi:10.3390/e24101425 · Cited by 11
Performance Analysis of YOLO Algorithms for Real-Time Crowd Counting
Ranjan, A., Pathare, N., Dhavale, S., & Kumar, S. (2022) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon55314.2022.9909018 · Cited by 12
Oza, P. R., Sharma, P., & Patel, S. (2022) · Scalable Computing Practice and Experience · doi:10.12694/scpe.v23i3.1992 · Cited by 11
An Efficient and Lightweight Method for Human Ear Recognition Based on MobileNet
Xu, X., Liu, Y., Cao, S., & Lu, L. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/9069007 · Cited by 11
Daytime Sea Fog Detection Based on a Two-Stage Neural Network
Tang, Y., Yang, P., Zhou, Z., et al. (2022) · Remote Sensing · doi:10.3390/rs14215570 · Cited by 11
A Survey of Glaucoma Detection Algorithms using Fundus and OCT Images
Kiefer, R., Steen, J., Abid, M., Ardali, M. R., & Amjadian, E. (2022) · IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) · doi:10.1109/iemcon56893.2022.9946629 · Cited by 13
Neural Networks for Improving Wind Power Efficiency: A Review
Shin, H., Rüttgers, M., & Lee, S.-S. (2022) · Fluids · doi:10.3390/fluids7120367 · Cited by 11
Zhang, Y., Sun, B., Jiang, W., Ha, Y., Hu, M., & Zhao, W. (2022) · International Conference on Computer Aided Design · doi:10.1145/3508352.3549439 · Cited by 11
Kim, T., Jeon, M., Lee, C., Kim, J., Ko, G., Kim, J.-Y., & Youn, C. (2022) · IEEE Access · doi:10.1109/access.2022.3219879 · Cited by 11
Sahoo, P., Saha, S., Mondal, S., & Sharma, N. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956499 · Cited by 10
Two-Stream Deep Learning Architecture-Based Human Action Recognition
Shehzad, F., Khan, M. A., Asfand-e-yar, M., et al. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.028743 · Cited by 11
Srivastava, P., Singh, S. P., Vishnoi, A., & Sapra, V. (2022) · International Conference on Advances in Computing, Communication and Materials (ICACCM) · doi:10.1109/icaccm56405.2022.10009476 · Cited by 12
Machine learning classification of non-Markovian noise disturbing quantum dynamics
Martina, S., Gherardini, S., & Caruso, F. (2023) · Physica Scripta · doi:10.1088/1402-4896/acb39b · Cited by 26
A Machine Learning Hybrid Approach for Diagnosing Plants Bacterial and Fungal Diseases
BaniMustafa, A., Qattous, H., Ghabeish, I. H., & Karajeh, M. R. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140198 · Cited by 12
Effati, M., & Nejat, G. (2023) · Computers · doi:10.3390/computers12020044 · Cited by 14
Object-centric Contour-aware Data Augmentation Using Superpixels of Varying Granularity
Dornaika, F., Sun, D., Hammoudi, K., Charafeddine, J., Cabani, A., & Zhang, C. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109481 · Cited by 11
Pan, Z., Zhou, S., Zou, H., Liu, C., Zang, M., Liu, T., & Wang, Q. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26489 · Cited by 11
Khan, N., Singh, A. V., & Agrawal, R. K. (2023) · Annals of Emerging Technologies in Computing · doi:10.33166/aetic.2023.02.002 · Cited by 14
Deep Learning Sparse Array Design Using Binary Switching Configurations
Hamza, S. A., Juretus, K., Amin, M. G., & Ahmad, F. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094662 · Cited by 11
HARFLOW3D: A Latency-Oriented 3D-CNN Accelerator Toolflow for HAR on FPGA Devices
Toupas, P., Montgomerie-Corcoran, A., Bouganis, C.-S., et al. (2023) · IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM) · doi:10.1109/fccm57271.2023.00024 · Cited by 11
A Spatial Distribution Extraction Method for Winter Wheat Based on Improved U-Net
Liu, J., Wang, H., Zhang, Y., Zhao, X., Qu, T., Tian, H., Lu, Y., Su, J., Luo, D., & Yang, Y. (2023) · Remote Sensing · doi:10.3390/rs15153711 · Cited by 12
A new multi‐object tracking pipeline based on computer vision techniques for mussel farms
Zeng, D., Liu, I., Bi, Y., Vennell, R., Briscoe, D. K., Xue, B., & Zhang, M. (2023) · Journal of the Royal Society of New Zealand · doi:10.1080/03036758.2023.2240466 · Cited by 11
CheckSORT: Refined Synthetic Data Combination and Optimized SORT for Automatic Retail Checkout
Shi, Z., Liu, Z., Liu, L., Liu, R., Yamamoto, T., Mi, X., & Uchida, D. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00569 · Cited by 7
Venus: A Versatile Deep Neural Network Accelerator Architecture Design for Multiple Applications
Yang, J., Zheng, H., & Louri, A. (2023) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac56929.2023.10247897 · Cited by 11
Neural Networks and Deep Learning
Hashemi, A., & Dowlatshahi, M. B. (2024) · Springer eBooks · doi:10.1007/978-981-97-3820-5_13 · Cited by 12
Computer Vision for Head Pose Estimation: Review of a Competition
Huttunen, H. J., Chen, K., Thakur, A., Krohn-Grimberghe, A., Gencoglu, O., Ni, X., Al-Musawi, M., Xu, L., & van Veen, H. J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19665-7_6 · Cited by 11
Deep learning with shallow architecture for image classification
ElAdel, A., Ejbali, R., Zaied, M., et al. (2015) · International Conference on High Performance Computing & Simulation (HPCS) · doi:10.1109/hpcsim.2015.7237069 · Cited by 11
Cross-media Topic Detection with Refined CNN based Image-Dominant Topic Model
Wang, Z., Li, L., & Huang, Q. (2015) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2733373.2806309 · Cited by 11
A novel efficient method for training sparse auto-encoders
Luo, Y., & Wan, Y. (2013) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2013.6745205 · Cited by 11
Deep learning vector quantization for acoustic information retrieval
Huang, Z., Weng, C., Li, K., et al. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6853817 · Cited by 11
Dynamically reconfigurable silicon array of generalized integrate-and-fire neurons
Varghese, V., Molin, J. L., Brändli, C., et al. (2015) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2015.7348393 · Cited by 11
Simplified LSTM unit and search space probability exploration for image description
Nina, O., & Rodríguez, A. (2015) · International Conference on Information, Communications & Signal Processing (ICICS) · doi:10.1109/icics.2015.7459976 · Cited by 11
Image Copy Detection Based on Convolutional Neural Networks
Zhang, J., Zhu, W., Li, B., Hu, W., & Yang, J. (2016) · Communications in computer and information science · doi:10.1007/978-981-10-3005-5_10 · Cited by 11
Performance evaluation of hybrid CNN for SIPPER plankton image calssification
Al-Barazanchi, H. A., Verma, A., & Wang, S. (2015) · International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip.2015.7460262 · Cited by 14
Cans and cants: Computational potentials for multimodality with a case study in head position
Voigt, R., Eckert, P., Jurafsky, D., & Podesva, R. J. (2016) · Journal of Sociolinguistics · doi:10.1111/josl.12216 · Cited by 11
Query expansion for object retrieval with active learning using BoW and CNN feature
Zhao, X. B., & Ding, G. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-4142-3 · Cited by 11
Measuring error-tolerance in SRAM architecture on hardware accelerated neural network
Kwon, S., Lee, K., Kim, Y., Kim, K., Lee, C., & Ro, W. W. (2016) · IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia) · doi:10.1109/icce-asia.2016.7804818 · Cited by 12
An ensemble of deep learning architectures for automatic feature extraction
Shaheen, F., & Verma, B. (2016) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2016.7850047 · Cited by 11
Refining deep convolutional features for improving fine-grained image recognition
Zhang, W., Yan, J., Shi, W., et al. (2017) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-017-0176-3 · Cited by 11
Finetuning Convolutional Neural Networks for visual aesthetics
Wang, Y., Li, Y., & Porikli, F. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900185 · Cited by 11
Convolutional neural networks with multi-valued neurons
Kominami, Y., Ogawa, H., & Murase, K. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966183 · Cited by 11
Binarized Mode Seeking for Scalable Visual Pattern Discovery
Zhang, W., Cao, X., Wang, R., et al. (2017) · 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.2017.722 · Cited by 11
An Efficient, Distributed Stochastic Gradient Descent Algorithm for Deep-Learning Applications
Cong, G., Bhardwaj, O., & Feng, M. (2017) · Proceedings of the International Conference on Parallel Processing · doi:10.1109/icpp.2017.10 · Cited by 11
Using Data Compression for Optimizing FPGA-Based Convolutional Neural Network Accelerators
Guan, Y., Xu, N., Zhang, C., Yuan, Z., & Cong, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67952-5_2 · Cited by 13
Chouiekh, A., & Elhaj, E. H. I. (2017) · International Conference on Intelligent Systems and Computer Vision (ISCV) · doi:10.1109/isacv.2017.8054923 · Cited by 11
Learning Feature Embedding with Strong Neural Activations for Fine-Grained Retrieval
Shen, C., Zhou, C., Jin, Z., et al. (2017) · doi:10.1145/3126686.3126708 · Cited by 11
Computer Aided Diagnosis in Ophthalmology: Deep Learning Applications
Galveia, J., Travassos, A., Quadros, F. A. D. A., & Cruz, L. A. D. S. (2017) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-3-319-65981-7_10 · Cited by 12
Li, M., Ling, C., Xu, Q., et al. (2017) · Amino Acids · doi:10.1007/s00726-017-2512-4 · Cited by 11
Pedestrian detection based on YOLOv2 with skip structure in underground coal mine
Wang, L., Li, W., Zhang, Y., et al. (2017) · IEEE Information Technology and Mechatronics Engineering Conference (ITOEC) · doi:10.1109/itoec.2017.8122550 · Cited by 11
Research on classification of architectural style image based on convolution neural network
Guo, K., & Li, N. (2017) · IEEE Information Technology and Mechatronics Engineering Conference (ITOEC) · doi:10.1109/itoec.2017.8122517 · Cited by 11
Pogrebnyakov, N., & Maldonado, E. A. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8258464 · Cited by 11
Deep convolutional neural network based unmanned surface vehicle maneuvering
Xu, Q., Zhang, C., & Zhang, L. (2017) · Chinese Automation Congress · doi:10.1109/cac.2017.8242889 · Cited by 12
Image Aesthetic Distribution Prediction with Fully Convolutional Network
Fang, H., Cui, C., Deng, X., Nie, X., Jian, M., & Yin, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-73603-7_22 · Cited by 11
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
