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 58,601–58,700 of 108,438
Millimeter-Wave Radar and Vision Fusion Target Detection Algorithm Based on an Extended Network
Qi, C., Song, C., Zhang, N., Song, S., Wang, X., & Xiao, F. (2022) · Machines · doi:10.3390/machines10080675 · Cited by 7
Unbiased quasi-hyperbolic nesterov-gradient momentum-based optimizers for accelerating convergence
Cheng, W., Yang, X., Wang, B., & Wang, W. (2022) · World Wide Web · doi:10.1007/s11280-022-01086-3 · Cited by 8
LSTM-Based Neural Network to Recognize Human Activities Using Deep Learning Techniques
Sabbu, S., & Ganesan, V. (2022) · Applied Computational Intelligence and Soft Computing · doi:10.1155/2022/1681096 · Cited by 7
Lake algal bloom monitoring via remote sensing with biomimetic and computational intelligence
Sun, Z., Chang, N., Chen, C.-F., et al. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.102991 · Cited by 7
Dikici, B., Bekçioğulları, M. F., Açıkgöz, H., & Korkmaz, D. (2022) · Konya Journal of Engineering Sciences · doi:10.36306/konjes.1078358 · Cited by 8
Evaluation of Respiratory Sounds Using Image-Based Approaches for Health Measurement Applications
Cohen-McFarlane, M., Xi, P., Wallace, B., Habashy, K., Huq, S., Goubran, R. A., & Knoefel, F. D. (2022) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2022.3202435 · Cited by 7
Imaging Domain Seismic Denoising Based on Conditional Generative Adversarial Networks (CGANs)
Zhang, H., & Wang, W. (2022) · Energies · doi:10.3390/en15186569 · Cited by 7
A novel deep learning package for electrocardiography research
Wen, H., & Kang, J. (2022) · Physiological Measurement · doi:10.1088/1361-6579/ac9451 · Cited by 7
Skowron, M., Kowalski, C. T., & Orłowska-Kowalska, T. (2022) · Energies · doi:10.3390/en15197008 · Cited by 9
SIL-Net: A Semi-Isotropic L-shaped network for dermoscopic image segmentation
Zhang, Z., Jiang, Y., Qiao, H., Wang, M., Yan, W., & Chen, J. (2022) · Comput. Biol. Medicine · doi:10.1016/j.compbiomed.2022.106146 · Cited by 8
A Deep Learning-Based Model for Automated Quality Control in the Pharmaceutical Industry
Raab, D., Fezer, E., Breitenbach, J., Baumgartl, H., Sauter, D., & Buettner, R. (2022) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac54236.2022.00045 · Cited by 8
Segmentation of tomography datasets using 3D convolutional neural networks
James, J., Pruyne, N., Stan, T., Schwarting, M., Yeom, J., Hong, S., Voorhees, P. W., Blaiszik, B. J., & Foster, I. T. (2022) · Computational Materials Science · doi:10.1016/j.commatsci.2022.111847 · Cited by 8
Do Vision-and-Language Transformers Learn Grounded Predicate-Noun Dependencies?
Nikolaus, M., Salin, E., Ayache, S., Fourtassi, A., & Favre, B. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.100 · Cited by 14
Label Diffusion Graph Learning network for semi-supervised breast histological image recognition
Zeng, Y., & Xu, X. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104306 · Cited by 7
Singh, J. M., & Ramachandra, R. (2022) · European Workshop on Visual Information Processing · doi:10.1109/euvip53989.2022.9922773 · Cited by 10
Quantifying Performance Gains of GPUDirect Storage
Inupakutika, D., Davis, B., Yang, Q., et al. (2022) · IEEE International Conference on Networking, Architecture and Storage (NAS) · doi:10.1109/nas55553.2022.9925516 · Cited by 7
Dual attention composition network for fashion image retrieval with attribute manipulation
Wan, Y., Zou, G., Yan, C., et al. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07994-9 · Cited by 7
A multi-frame network model for predicting seizure based on sEEG and iEEG data
Lu, L., Zhang, F., Wu, Y., Ma, S., Zhang, X., & Ni, G. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.1059565 · Cited by 9
Influence of Digital Fluctuations on Behavior of Neural Networks
Netay, I. V. (2022) · Indian Journal of Artificial Intelligence and Neural Networking · doi:10.54105/ijainn.a1061.123122 · Cited by 5
Persis: A Persian Font Recognition Pipeline Using Convolutional Neural Networks
Mohammadian, M., Maleki, N., Olsson, T., & Ahlgren, F. (2022) · International Conference on Computer and Knowledge Engineering · doi:10.1109/iccke57176.2022.9960037 · Cited by 7
Kushwaha, A., Srivastava, P., & Khare, A. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7571 · Cited by 7
Abbas, A., Gaber, M. M., & Abdelsamea, M. M. (2022) · Sensors · doi:10.3390/s22249875 · Cited by 9
Self-attention Networks for Non-recurrent Handwritten Text Recognition
d’Arce, R., Norton, T. J. T., Hannuna, S., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21648-0_27 · Cited by 7
NeuroUnlock: Unlocking the Architecture of Obfuscated Deep Neural Networks
Ahmadi, M. M., Alrahis, L., Colucci, A., Sinanoglu, O., & Shafique, M. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892545 · Cited by 8
Wang, H., Wang, J., Shen, Z., Zhang, Z., Li, J., Zhao, L., Jiao, S., Li, S., Lei, Y., Kou, W., et al. (2022) · International Journal of Remote Sensing · doi:10.1080/01431161.2022.2093622 · Cited by 8
CCFE: A Few-Shot Learning Model for Earthquake Detection and Phase Identification
Peng, Z., Yongqian, S., & Shian, X. (2022) · IEEE Access · doi:10.1109/access.2022.3225171 · Cited by 7
A General Framework for Accelerator Management Based on ISA Extension
Cheshmikhani, E., Peccerillo, B., Mondelli, A., & Bartolini, S. (2022) · IEEE Access · doi:10.1109/access.2022.3222346 · Cited by 8
Number of Attention Heads vs. Number of Transformer-encoders in Computer Vision
Hrycej, T., Bermeitinger, B., & Handschuh, S. (2022) · International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management · doi:10.5220/0011578000003335 · Cited by 10
Vidyarthi, A. (2022) · Cognitive Computation · doi:10.1007/s12559-022-10090-8 · Cited by 7
Islam, M. R., Siddique, M. A. M. T., Amiruzzaman, M., et al. (2023) · Annals of Emerging Technologies in Computing · doi:10.33166/aetic.2023.01.001 · Cited by 7
Luo, J., Wang, H., Tong, S., et al. (2023) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2401007 · Cited by 7
Vengalil, S. K., Krishnamurthy, B., & Sinha, N. (2023) · Frontiers in Signal Processing · doi:10.3389/frsip.2022.936875 · Cited by 7
EA-UNet Based Segmentation Method for OCT Image of Uterine Cavity
Zhang, X., Du, M., Liu, J., Sun, E., Zhang, J., Gong, X., & Chen, Z. (2023) · Photonics · doi:10.3390/photonics10010073 · Cited by 8
Li, F., Long, X., Dai, J., Li, C., Dong, X., & He, J. (2023) · Applied Optics · doi:10.1364/ao.478540 · Cited by 8
Deep Learning Methods for Real-time Detection and Analysis of Wagner Ulcer Classification System
Han, A., Zhang, Y., Li, A., Li, C., Zhao, F., Dong, Q., Liu, Y., Shen, X., Yan, S., & Zhou, S. (2022) · doi:10.1109/ccat56798.2022.00010 · Cited by 10
Qin, S.-M., Li, M., Xu, T., & Dong, S.-Q. (2023) · Physica Scripta · doi:10.1088/1402-4896/acb46c · Cited by 7
Lexicon and attention based handwritten text recognition system
Kumari, L., Singh, S., Rathore, V. V. S., et al. (2022) · Machine Graphics and Vision · doi:10.22630/mgv.2022.31.1.4 · Cited by 7
Slum image detection and localization using transfer learning: a case study in Northern Morocco
Moudden, T. E., Dahmani, R., Amnai, M., & Fora, A. A. (2023) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v13i3.pp3299-3310 · Cited by 8
End-to-End Learning for Visual Navigation of Forest Environments
Niu, C., Zauner, K., & Tarapore, D. (2023) · Forests · doi:10.3390/f14020268 · Cited by 7
Schulz, T., Becker, C., & Kayser, G. (2023) · HNO · doi:10.1007/s00106-023-01276-z · Cited by 7
Gaussian Blur Masked ResNet2.0 Architecture for Diabetic Retinopathy燚etection
Boruah, S., Dehloo, A., Gupta, P., Prusty, M. R., & Balasundaram, A. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.035143 · Cited by 8
Baldeon-Calisto, M., Wei, Z., Abudalou, S., Yılmaz, Y., Gage, K. L., Pow‐Sang, J. M., & Balagurunathan, Y. (2023) · Frontiers in Nuclear Medicine · doi:10.3389/fnume.2022.1083245 · Cited by 9
A Systematic and Bibliometric Review on Face Recognition: Convolutional Neural Network
Aakanksha, A., Singh, G., Singh, J., & Verma, D. (2022) · International Conference on Automation, Computing and Renewable Systems (ICACRS) · doi:10.1109/icacrs55517.2022.10029115 · Cited by 8
Dewangan, N., Kumar, A., & Patel, R. N. (2023) · Cybernetics & Systems · doi:10.1080/01969722.2023.2175120 · Cited by 7
Inverse Design of Micro Phononic Beams Incorporating Size Effects via Tandem Neural Network
Li, J., Miao, Z., Li, S., & Ma, Q. (2023) · Materials · doi:10.3390/ma16041518 · Cited by 7
Latent Class-Conditional Noise Model
Yao, J., Han, B., Zhou, Z., Zhang, Y., & Tsang, I. W.-H. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3247629 · Cited by 8
Intelligent identification of pavement cracks based on PSA-Net
Lin, X., Zhang, J., Wu, D., et al. (2023) · Frontiers in Environmental Science · doi:10.3389/fenvs.2023.1105467 · Cited by 7
Jeon, D., & Kim, M. S. (2023) · Electronics · doi:10.3390/electronics12051115 · Cited by 7
Model pruning based on filter similarity for edge device deployment
Wu, T., Song, C., & Zeng, P. F. (2023) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2023.1132679 · Cited by 7
Restoration of Spatially Inhomogeneous Images Based on a Doubly Stochastic Model
Andriyanov, N. A., Vasiliev, K. K., Dementiev, V., et al. (2022) · Optoelectronics Instrumentation and Data Processing · doi:10.3103/s8756699022050028 · Cited by 7
SEQUENT: Towards Traceable Quantum Machine Learning Using Sequential Quantum Enhanced Training
Altmann, P. J., Sünkel, L., Stein, J., Müller, T., Roch, C., & Linnhoff–Popien, C. (2023) · International Conference on Agents and Artificial Intelligence (ICAART) · doi:10.5220/0011772400003393 · Cited by 7
Zhang, Y., Wang, S., Zhai, Z., Shang, Y., Viegut, R., Webb, E. B., Raedeke, A. H., & Sartwell, J. (2022) · IEEE International Conference on Cognitive Machine Intelligence (CogMI) · doi:10.1109/cogmi56440.2022.00026 · Cited by 7
Awujoola, O. J., Ogwueleka, F. N., Odion, P. O., Awujoola, A. E., & Adelegan, O. R. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-98352-5.00013-6 · Cited by 9
Soysal, O. A., Güzel, M. S., Dikmen, M., & Bostancı, E. (2023) · IEEE Access · doi:10.1109/access.2023.3259062 · Cited by 8
An Image Recommendation Algorithm Based on Target Alternating Attention and User Affiliation Network
Wan, S., Yang, S., Liu, Y., Ding, J., Qiu, D., & Wei, C. (2023) · Applied Sciences · doi:10.3390/app13074389 · Cited by 7
Chetouane, A., & Karoui, K. (2022) · doi:10.1109/iucc-cit-dsci-smartcns57392.2022.00035 · Cited by 7
Dong, Y., Li, Y., & Li, Z. (2023) · Electronics · doi:10.3390/electronics12071732 · Cited by 7
Knowledge Distillation With Feature Self Attention
Park, S.-G., & Kang, D. (2023) · IEEE Access · doi:10.1109/access.2023.3265382 · Cited by 7
Arias-Londoño, J. D., Moure-Prado, Á., & Godino-Llorente, J. I. (2023) · Diagnostics · doi:10.3390/diagnostics13081381 · Cited by 7
Instance-prototype similarity consistency for unsupervised 2D image-based 3D model retrieval
Li, W., Li, W., Zhang, Y., et al. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103372 · Cited by 7
Panneerselvam, R., & Balasubramaniam, S. (2023) · Acta Informatica Pragensia · doi:10.18267/j.aip.211 · Cited by 7
Pixel-CRN: A New Machine Learning Approach for Convective Storm Nowcasting
Zhang, W., Chen, H., Han, L., Zhang, R., & Ge, Y. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3267976 · Cited by 9
URMP: using reconfigurable multicast path for NoC-based deep neural network accelerators
Ouyang, Y., Wang, J., Sun, C., et al. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05255-7 · Cited by 7
Yin, H., Yu, J., Shi, F., Xue, F., Shi, J., Huang, P., & Hou, D. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3267488 · Cited by 7
A Fusion of Three Custom-Tailored Deep Learning Architectures for Waste Classification
Rahman, N., & Das, S. K. (2022) · International Conference on Sustainable Technologies for Industry 5.0 (STI) · doi:10.1109/sti56238.2022.10103297 · Cited by 10
Forecasting of Solar Photovoltaic Output Energy using LSTM Machine Learning Algorithm
Tamal, M. B. A., Alam, M. A., Sharker, M. N., & Sazib, M. I. (2022) · International Conference on Sustainable Technologies for Industry 5.0 (STI) · doi:10.1109/sti56238.2022.10103310 · Cited by 10
Finger gesture recognition with smart skin technology and deep learning
Ben-Ari, L., Ben-Ari, A., Hermon, C., & Hanein, Y. (2023) · Flexible and Printed Electronics · doi:10.1088/2058-8585/acd2e8 · Cited by 9
Edwards, C., Vaske, A., McDaniel, N., et al. (2023) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2023.3273175 · Cited by 7
Few-Shot Partial Multi-View Learning
Zhou, Y., Guo, Y., Hao, S., Hong, R., & Luo, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3275162 · Cited by 8
Method for the automatic recognition of cropland headland images based on deep learning
Qiao, Y., Liu, H., Meng, Z., Chen, J., & Ma, L. (2023) · International journal of agricultural and biological engineering · doi:10.25165/j.ijabe.20231602.6195 · Cited by 7
Summarizing vehicle driving decision-making methods on vulnerable road user collision avoidance
Yuan, Q., Gao, Y., Zhu, J., et al. (2023) · Digital Transportation and Safety · doi:10.48130/dts-2023-0003 · Cited by 7
Serial Dependence in Dermatological Judgments
Ren, Z., Li, X., Pietralla, D., Manassi, M., & Whitney, D. C. (2023) · Diagnostics · doi:10.3390/diagnostics13101775 · Cited by 7
Alizadeh, N., Afrakhteh, S., & Mosavi, M. R. (2023) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22913 · Cited by 10
Recognition of Tamil handwritten characters using Scrabble GAN
Sasipriyaa, N., Natesan, P., Gothai, E., et al. (2023) · International Conference on Computer Communication and Informatics (ICCCI) · doi:10.1109/iccci56745.2023.10128564 · Cited by 7
Masuda, K., Kuwada, E., Suzuki, M., Suzuki, T., Niikawa, T., Uchida, S., & Akagi, T. (2023) · Plant and Cell Physiology · doi:10.1093/pcp/pcad050 · Cited by 7
Prediction of Heart Diseases Using Data Mining Algorithms
Al-Jammali, K. (2023) · Informatica · doi:10.31449/inf.v47i5.4467 · Cited by 7
Ocean Front Detection With Bi-Directional Progressive Fusion Attention Network
Zhu, J., Li, Q., Xie, C., et al. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3279783 · Cited by 7
Model Poisoning Attack on Neural Network Without Reference Data
Zhang, X., Zhang, H., Zhang, G., Li, H., Yu, D., Cheng, X., & Hu, P. (2023) · IEEE Transactions on Computers · doi:10.1109/tc.2023.3280133 · Cited by 7
Detection and Localization of Glioma and Meningioma Tumors in Brain MR Images using Deep Learning
Cengil, E., Eroğlu, Y., Çınar, A., et al. (2023) · Sakarya University Journal of Science · doi:10.16984/saufenbilder.1067061 · Cited by 7
Source Detection With Multi-Label Classification
Vijayamohanan, J., Gupta, A. K., Noakoasteen, O., et al. (2023) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2023.3280854 · Cited by 7
Liang, B.-S. (2023) · International Symposium on VLSI Technology, Systems and Applications (VLSI-TSA) · doi:10.1109/vlsi-tsa/vlsi-dat57221.2023.10134466 · Cited by 7
The Application of Augmented Reality Technology in Urban Greening Plant Growth State Detection
Xing, H., Deng, F., Tang, Y., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3284550 · Cited by 7
Semantic segmentation via pixel‐to‐center similarity calculation
Wu, D., Guo, Z., Li, A., et al. (2023) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12245 · Cited by 7
Lightweight computational spectrometer enabled by learned high-correlation optical filters
Liu, Z., Liao, H., Yang, L., Du, G., Wei, L., Wang, Y., & Chen, Y. (2023) · Optics Express · doi:10.1364/oe.495087 · Cited by 7
Анализ экспериментальных теневых изображений течений методами компьютерного зрения
Doroshchenko, I. A. (2023) · Vyčislitelʹnye metody i programmirovanie · doi:10.26089/nummet.v24r217 · Cited by 7
Multiscale Feature Fusion for Hyperspectral Marine Oil Spill Image Segmentation
Chen, G., Huang, J., Wen, T., Du, C., Lin, Y.-T., & Xiao, Y. (2023) · Journal of Marine Science and Engineering · doi:10.3390/jmse11071265 · Cited by 7
Facial emotion recognition using convolutional neural network based krill head optimisation
Devi, B., & Preetha, M. M. S. J. (2023) · Expert Systems · doi:10.1111/exsy.13376 · Cited by 7
An Improved Signature Forgery Detection using Modified CNN in Siamese Network
Harris, S. K. J., & Anitha, J. (2023) · International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) · doi:10.1109/iceeict56924.2023.10156951 · Cited by 7
Li, S.-F., Huang, M., & Wu, Y. (2023) · Mathematics · doi:10.3390/math11132841 · Cited by 9
Vessel Trajectory Similarity Computation Based on Heterogeneous Graph Neural Network
Luo, S., & Zeng, W. (2023) · Journal of Marine Science and Engineering · doi:10.3390/jmse11071318 · Cited by 7
A waste classification method based on a capsule network
Huang, L., Li, M., Xu, T., & Dong, S. (2023) · Environmental Science and Pollution Research · doi:10.1007/s11356-023-27970-7 · Cited by 8
Semi-Dense U-Net: A Novel U-Net Architecture for Face Detection
Pai, G. A. V., & Sharmila Kumari, M. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140643 · Cited by 7
MeT: mesh transformer with an edge
Zhou, P., Dong, X., Cao, J., & Chen, Z. (2023) · The Visual Computer · doi:10.1007/s00371-023-02966-z · Cited by 7
Data enhancement and multi-feature learning model for pest classification
Zhao, S., Sun, X., & Gai, L. (2023) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-230606 · Cited by 7
Alam, M. U., Hollmén, J., Baldvinsson, J. R., & Rahmani, R. (2023) · Nordic Machine Intelligence · doi:10.5617/nmi.10471 · Cited by 7
Chen, Y., Wang, X., Chen, Z., Wang, K., Sun, Y., Jiang, J., & Liu, X. (2023) · Plants · doi:10.3390/plants12142701 · Cited by 6
Shabbir, N., & Rout, R. K. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104770 · Cited by 7
Nugraha, K. O. P. P., & Rifai, A. P. (2023) · Jurnal Ilmiah Teknik Industri · doi:10.23917/jiti.v22i1.21826 · Cited by 14
Suspicious Activities Recognition in Video Sequences Using DarkNet-NasNet Optimal Deep Features
Khan, S., Khan, M. A., Shah, J. H., et al. (2023) · Computer Systems Science and Engineering · doi:10.32604/csse.2023.040410 · Cited by 7
Modelling the Tumour Microenvironment, but What Exactly Do We Mean by “Model”?
Reyes‐Aldasoro, C. C. (2023) · Cancers · doi:10.3390/cancers15153796 · Cited by 8
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
