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 36,401–36,500 of 108,435
Deep Learning and Thresholding with Class-Imbalanced Big Data
Johnson, J. M., & Khoshgoftaar, T. M. (2019) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2019.00134 · Cited by 24
Fungi Recognition: A Practical Use Case
Šulc, M., Picek, L., Matas, J., Jeppesen, T. S., & Heilmann‐Clausen, J. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093624 · Cited by 21
An Efficient and Effective Second-Order Training Algorithm for LSTM-Based Adaptive Learning
Vural, N. M., Ergüt, S., & Kozat, S. S. (2021) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2021.3071566 · Cited by 23
Compressed Holistic ConvNet Representations for Detecting Loop Closures in Dynamic Environments
Wang, S., Lv, X., Liu, X., & Ye, D. (2020) · IEEE Access · doi:10.1109/access.2020.2982228 · Cited by 21
Recognizing actions in images by fusing multiple body structure cues
Li, Y., Li, K., Li, K., et al. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107341 · Cited by 21
Lithography Hotspot Detection with FFT-based Feature Extraction and Imbalanced Learning Rate
He, X., Deng, Y., Zhou, S., Li, R., Wang, Y., & Guo, Y. (2019) · ACM Transactions on Design Automation of Electronic Systems · doi:10.1145/3372044 · Cited by 22
Brain MR Image Classification Based on Deep Features by Using Extreme Learning Machines
Arı, A. (2020) · Biomedical Journal of Scientific & Technical Research · doi:10.26717/bjstr.2020.25.004201 · Cited by 21
Hollon, T. C., & Orringer, D. A. (2020) · Molecular & Cellular Oncology · doi:10.1080/23723556.2020.1736742 · Cited by 20
Shawon, A., Faruk, O., Habib, M. B., et al. (2019) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc47050.2019.9064029 · Cited by 21
EdgeFool: An Adversarial Image Enhancement Filter
Shahin, A., Oh, C., Cavallaro, A., et al. (2020) · Queen Mary Research Online (Queen Mary University of London) · Cited by 21
Robust pruning for efficient CNNs
Ide, H., Kobayashi, T., Watanabe, K., & Kurita, T. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.03.034 · Cited by 22
Detecting Adversarial Attacks In Time-Series Data
Abdu-Aguye, M. G., Gomaa, W. E., Makihara, Y., & Yagi, Y. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053311 · Cited by 25
Deep-Learning Ensemble for Offline Arabic Handwritten Words Recognition
Awni, M., Khalil, M. I., & Abbas, H. M. (2019) · International Conference on Computer Engineering and Systems · doi:10.1109/icces48960.2019.9068184 · Cited by 21
Feng, T., Liu, J., Fang, X., Wang, J., & Zhou, L. (2020) · Sensors · doi:10.3390/s20082360 · Cited by 22
Evolving conductive polymer neural networks on wetware
Akai‐Kasaya, M., Hagiwara, N., Hikita, W., Okada, M., Sugito, Y., Kuwahara, Y., & Asai, T. (2020) · Japanese Journal of Applied Physics · doi:10.35848/1347-4065/ab8e06 · Cited by 22
Neural Network Pricing of American Put Options
Gaspar, R. M., Lopes, S. D., & Sequeira, B. (2020) · Risks · doi:10.3390/risks8030073 · Cited by 18
The Development of Silicon for AI: Different Design Approaches
Lee, K. J., Lee, J., Choi, S., & Yoo, H. (2020) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2020.2996625 · Cited by 22
MultiDIAL: Domain Alignment Layers for (Multisource) Unsupervised Domain Adaptation
Carlucci, F. M., Porzi, L., Barbara Caputo, B., Ricci, E., & Bulò, S. R. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3001338 · Cited by 22
Manasi, S. D., Snigdha, F. S., & Sapatnekar, S. S. (2020) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2020.2995135 · Cited by 20
Comparing CNN-Based Object Detectors on Two Novel Maritime Datasets
Soloviev, V., Farahnakian, F., Zelioli, L., et al. (2020) · IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew46912.2020.9106019 · Cited by 21
Learning Augmentation Network via Influence Functions
Lee, D., Park, H., Pham, T. X., & Yoo, C. D. (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/cvpr42600.2020.01097 · Cited by 22
SMT Assembly Inspection Using Dual-Stream Convolutional Networks and Two Solder Regions
Kim, Y.-G., & Park, T.-H. (2020) · Applied Sciences · doi:10.3390/app10134598 · Cited by 22
Toward Invisible Adversarial Examples Against DNN-Based Privacy Leakage for Internet of Things
Ding, X., Zhang, S., Song, M., Ding, X., & Li, F. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3008232 · Cited by 21
Improving Object Detection Performance Using Scene Contextual Constraints
Alamri, F., & Pugeault, N. (2020) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.3008213 · Cited by 22
Facial Expression Recognition Based on Optimized ResNet
Zhong, Y., Qiu, S., Luo, X., et al. (2020) · doi:10.1109/wsai49636.2020.9143287 · Cited by 21
Convolutional Neural Network-Based Dictionary Learning for SAR Target Recognition
Tao, L., Zhou, Y., Jiang, X., et al. (2020) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2020.3008212 · Cited by 21
OCT Image Recognition of Cardiovascular Vulnerable Plaque Based on CNN
Wang, J. (2020) · IEEE Access · doi:10.1109/access.2020.3007599 · Cited by 21
A peridynamic-based machine learning model for one-dimensional and two-dimensional structures
Nguyen, C. T., Oterkus, S., & Oterkus, E. (2020) · Continuum Mechanics and Thermodynamics · doi:10.1007/s00161-020-00905-0 · Cited by 21
An Edge Computing Based Fall Detection System for Elderly Persons
Chen, Y., Kong, X., Meng, L., & Tomiyama, H. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.06.049 · Cited by 22
Complex Emotion Profiling: An Incremental Active Learning Based Approach With Sparse Annotations
Thuseethan, S., Rajasegarar, S., & Yearwood, J. L. (2020) · IEEE Access · doi:10.1109/access.2020.3015917 · Cited by 21
PIG FACE IDENTIFICATION BASED ON IMPROVED ALEXNET MODEL
Yan, H., Cui, Q., & Liu, Z. (2020) · INMATEH Agricultural Engineering · doi:10.35633/inmateh-61-11 · Cited by 21
Park, S., & Kwon, J. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3015948 · Cited by 20
Sadak, F., Saadat, M., & Hajiyavand, A. M. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103976 · Cited by 19
Qi, Q., Lin, X., Chen, C., Xie, W., Huang, Y., Ding, X., Liu, X., & Yu, Y. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3021558 · Cited by 21
Mzoughi, H., Njeh, I., Slima, M. B., et al. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09786-6 · Cited by 21
Micro-Expression Recognition Based on 2D-3D CNN
Wang, L., Jia, J., & Mao, N. (2020) · Chinese Control Conference (CCC) · doi:10.23919/ccc50068.2020.9188920 · Cited by 20
Embedded vision system for monitoring arc welding with thermal imaging and deep learning
Martínez, A. F., Souto-López, Á., González, C. G., et al. (2020) · doi:10.1109/coins49042.2020.9191650 · Cited by 21
Chen, H., He, Z., Zhang, Z., Geng, Y., & Yu, W. (2020) · Optics Express · doi:10.1364/oe.403316 · Cited by 22
Miyagi, Y., Habara, T., Hirata, R., & Hayashi, N. (2020) · Artificial Intelligence in Medical Imaging · doi:10.35711/aimi.v1.i3.94 · Cited by 24
Rodriguez, M. A., Sotomonte, J. F., Cifuentes, J., et al. (2020) · International Conference on Smart Energy Systems and Technologies (SEST) · doi:10.1109/sest48500.2020.9203082 · Cited by 21
Kang, Y.-W., Wu, C.-F., Chang, Y.-H., et al. (2020) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2020.3012250 · Cited by 21
Giudice, M. L., Varone, G., Ieracitano, C., et al. (2020) · doi:10.1109/ijcnn48605.2020.9207195 · Cited by 21
Liu, B., Du, S., Du, S., et al. (2020) · Remote Sensing · doi:10.3390/rs12183007 · Cited by 21
A New Technique for Diagnosis of Dental Caries on the Children’s First Permanent Molar
Yu, H., Lin, Z., Liu, Y., Su, J., Chen, B., & Lu, G. (2020) · IEEE Access · doi:10.1109/access.2020.3029454 · Cited by 21
Deng, J., Liu, J., Wu, C., Zhong, T., Gu, G., & Ling, B. W. (2020) · IEEE Access · doi:10.1109/access.2020.3032164 · Cited by 22
Deep-Learning-Based Vuggy Facies Identification from Borehole Images
Jiang, J., Xu, R., James, S. C., & Xu, C. (2020) · SPE Reservoir Evaluation & Engineering · doi:10.2118/204216-pa · Cited by 20
Hasan, M. M., Srizon, A. Y., & Hasan, M. A. M. (2020) · IEEE Region 10 Symposium (TENSYMP) · doi:10.1109/tensymp50017.2020.9230658 · Cited by 21
Zhang, A., Teng, L., & Alterovitz, G. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa233 · Cited by 21
Venu, S. K. (2021) · International Conference on Agents and Artificial Intelligence (ICAART) · doi:10.5220/0010377403900401 · Cited by 27
Soleimani, M., Vahidi, A., & Vaseghi, B. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.3040627 · Cited by 21
Water Level Estimation in Sewer Pipes Using Deep Convolutional Neural Networks
Haurum, J. B., Bahnsen, C., Pedersen, M., & Moeslund, T. B. (2020) · Water · doi:10.3390/w12123412 · Cited by 21
Collapsed Building Detection Using 3D Point Clouds and Deep Learning
Xiu, H., Shinohara, T., Matsuoka, M., Inoguchi, M., Kawabe, K., & Horie, K. (2020) · Remote Sensing · doi:10.3390/rs12244057 · Cited by 24
Time–Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems
Jiang, W., Wu, X., Wang, Y., Chen, B., Feng, W., & Jin, Y. (2021) · Sensors · doi:10.3390/s21010231 · Cited by 24
Multi-view Convolutional Network for Crowd Counting in Drone-Captured Images
Castellano, G., Castiello, C., Cianciotta, M., Mencar, C., & Vessio, G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66823-5_35 · Cited by 22
Transformation-Aware Embeddings for Image Provenance
Bharati, A., Moreira, D., Flynn, P. J., Rocha, A. D. R., Bowyer, K. W., & Scheirer, W. J. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3050061 · Cited by 23
Classification of Chicken Meat Freshness using Convolutional Neural Network Algorithms
Calvin, C., Putra, G. B., & Prakasa, E. (2020) · International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) · doi:10.1109/3ict51146.2020.9312018 · Cited by 21
Quality Control of PET Bottles Caps with Dedicated Image Calibration and Deep Neural Networks
Malesa, M., & Rajkiewicz, P. (2021) · Sensors · doi:10.3390/s21020501 · Cited by 28
Zhong, H. X., Wang, Y., Qin, X., Yin, R., Qiu, Y., He, K., & Zhu, Z. (2021) · Medical Physics · doi:10.1002/mp.14719 · Cited by 23
Zhou, Q., Wang, J., Liu, J., Li, S., Ou, W., & Jin, X. (2021) · Mobile Networks and Applications · doi:10.1007/s11036-020-01723-z · Cited by 21
Weighted Mask R‐CNN for Improving Adjacent Boundary Segmentation
Suh, S., Park, Y., Ko, K., et al. (2021) · Journal of Sensors · doi:10.1155/2021/8872947 · Cited by 21
MODALS: Modality-agnostic Automated Data Augmentation in the Latent Space
Cheung, T.-H., & Yeung, D. (2021) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · Cited by 32
An Efficient Pest Classification In Smart Agriculture Using Transfer Learning
Nguyen, T. T., Vien, Q., & Sellahewa, H. (2021) · EAI Endorsed Transactions on Industrial Networks and Intelligent Systems · doi:10.4108/eai.26-1-2021.168227 · Cited by 23
SAN: Selective Alignment Network for Cross-Domain Pedestrian Detection
Jiao, Y., Yao, H., & Xu, C. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3049948 · Cited by 21
Ahmed, M., & Laskar, R. H. (2021) · Multimedia Systems · doi:10.1007/s00530-020-00744-8 · Cited by 22
Obtaining leaner deep neural networks for decoding brain functional connectome in a single shot
Gupta, S., Chan, Y. H., Rajapakse, J. C., & Initiative, T. A. D. N. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.152 · Cited by 22
Changdar, S., Bhaumik, B., & De, S. (2021) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwab001 · Cited by 21
Detection of Pneumonia with a Novel CNN-based Approach
Erdem, E., & Aydın, T. (2020) · Sakarya University Journal of Computer and Information Sciences · doi:10.35377/saucis.04.01.787030 · Cited by 27
Utilizing cloud FPGAs towards the open neural network standard
Danopoulos, D., Kachris, C., & Soudris, D. (2021) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2021.100520 · Cited by 23
ResNet-Based Model for Autonomous Vehicles Trajectory Prediction
Zhang, Z. (2021) · 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) · doi:10.1109/iccece51280.2021.9342418 · Cited by 20
A New Deep Dual Temporal Domain Adaptation Method for Online Detection of Bearings Early Fault
Mao, W., Sun, B., & Wang, L. (2021) · Entropy · doi:10.3390/e23020162 · Cited by 21
Building Recognition of UAV Remote Sensing Images by Deep Learning
Zheng, L., Ai, P., & Wu, Y. (2020) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss39084.2020.9323322 · Cited by 23
How Convolutional Neural Networks Deal with Aliasing
Ribeiro, A. H., & Schön, T. B. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414627 · Cited by 24
Aerial-DeepSearch: Distributed Multi-Agent Deep Reinforcement Learning for Search Missions
Sadhu, V., Sun, C., Karimian, A., Tron, R., & Pompili, D. (2020) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass50613.2020.00030 · Cited by 22
Context-driven detection of distracted driving using images from in-car cameras
Dey, A. K., Goel, B., & Chellappan, S. (2021) · Internet of Things · doi:10.1016/j.iot.2021.100380 · Cited by 21
Point Cloud Semantic Segmentation Network Based on Multi-Scale Feature Fusion
Du, J., Jiang, Z., Huang, S., Wang, Z., Su, J., Su, S., Wu, Y., & Cai, G. (2021) · Sensors · doi:10.3390/s21051625 · Cited by 21
Second-order encoding networks for semantic segmentation
Sun, Q., Zhang, Z., & Li, P. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.003 · Cited by 21
A new hardware Trojan detection technique using deep convolutional neural network
Sharma, R., Rathor, V. S., Sharma, G. K., & Pattanaik, M. (2021) · Integration · doi:10.1016/j.vlsi.2021.03.001 · Cited by 25
A Novel Upsampling and Context Convolution for Image Semantic Segmentation
Sediqi, K. M., & Lee, H. J. (2021) · Sensors · doi:10.3390/s21062170 · Cited by 23
An Introductory Survey on Attention Mechanisms in Computer Vision Problems
Sun, J., Jiang, J., & Liu, Y. (2020) · International Conference on Big Data and Information Analytics (BigDIA) · doi:10.1109/bigdia51454.2020.00054 · Cited by 21
Automated Recognition of Weld Pool Characteristics from Active Vision Sensing
American Welding Society, Cheng, Y., Wang, Q., Jiao, W., Xiao, J., Chen, S., & Zhang, Y. (2021) · Welding Journal · doi:10.29391/2021.100.015 · Cited by 23
Ayensa-Jiménez, J., Doweidar, M. H., Sanz‐Herrera, J. A., & Doblaré, M. (2021) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2021.113816 · Cited by 21
Multi-Scale Attention-Guided Network for mammograms classification
Xu, C., Lou, M., Qi, Y., et al. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102730 · Cited by 21
SapAugment: Learning A Sample Adaptive Policy for Data Augmentation
Hu, T.-Y., Shrivastava, A., Chang, J.-H. R., Koppula, H. S., Braun, S., Hwang, K., Kalinli, O., & Tuzel, O. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413928 · Cited by 24
F-Net: Fusion Neural Network for Vehicle Trajectory Prediction in Autonomous Driving
Wang, J., Wang, P., Zhang, C., Su, K., & Li, J. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413881 · Cited by 24
Yang, Y., Song, H., Sun, S., Zhang, W., Chen, Y., Rakai, L., & Fang, Y. (2021) · Journal of Real-Time Image Processing · doi:10.1007/s11554-021-01121-y · Cited by 22
Hong, X., Duan, L., & Zhang, L. (2021) · Measurement Science and Technology · doi:10.1088/1361-6501/ac05f6 · Cited by 21
Dilated U-Block for Lightweight Indoor Depth Completion With Sobel Edge
Zhao, T., Pan, S., Zhang, H., et al. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3092280 · Cited by 21
Target Detection in Remote Sensing Image Based on Object-and-Scene Context Constrained CNN
Cheng, B., Li, Z., Xu, B., Dang, C., & Deng, J. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2021.3087597 · Cited by 21
Wan, H., Chen, J., Huang, Z., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3093308 · Cited by 21
Zhu, J., Bai, Y., Xu, Z., et al. (2021) · ACM Transactions on Graphics · doi:10.1145/3450626.3459798 · Cited by 21
Ma, W., Zhang, Y., Guo, J., & Li, K. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.08.005 · Cited by 22
A fusing framework of shortcut convolutional neural networks
Zhang, T., Waqas, M. M., Liu, Z., Tu, S., Halim, Z., Rehman, S. U., Li, Y., & Han, Z. (2021) · Information Sciences · doi:10.1016/j.ins.2021.08.030 · Cited by 22
Atrous spatial pyramid convolution for object detection with encoder-decoder
Jie, F., Nie, Q., Li, M., Yin, M., & Jin, T. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.064 · Cited by 21
Exploring the Quality of GAN Generated Images for Person Re-Identification
Jiang, Y., Chen, W., Sun, X., et al. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475547 · Cited by 21
An end-to-end deep learning model for robust smooth filtering identification
Zhang, Y., Luo, Y., Fang, Z., et al. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.09.004 · Cited by 21
Few-Shot Website Fingerprinting Attack with Data Augmentation
Chen, M., Wang, Y., Qin, Z., & Zhu, X. (2021) · Security and Communication Networks · doi:10.1155/2021/2840289 · Cited by 22
Learning Spherical Convolution for 360 Recognition
Su, Y.-C., & Grauman, K. L. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3113612 · Cited by 21
SECDA: Efficient Hardware/Software Co-Design of FPGA-based DNN Accelerators for Edge Inference
Haris, J., Gibson, P., Cano, J., Agostini, N. B., & Kaeli, D. R. (2021) · International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) · doi:10.1109/sbac-pad53543.2021.00015 · Cited by 26
SRDN: A Unified Super-Resolution and Motion Deblurring Network for Space Image Restoration
Yang, X., Wang, X., Wang, N., et al. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3131264 · Cited by 21
Anari, M. S., Rezaee, K., & Ahmadi, A. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12295-3 · 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
