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 65,901–66,000 of 108,435
HM_ADET: a hybrid model for automatic detection of eyelid tumors based on photographic images
Jiang, J., Liu, H., He, L., et al. (2024) · BioMedical Engineering OnLine · doi:10.1186/s12938-024-01221-3 · Cited by 6
Chen, M., Jiang, W., & Zhou, Y. (2024) · Remote Sensing · doi:10.3390/rs16050844 · Cited by 5
Xiao, H., Shi, X., Xia, Q., et al. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09545-w · Cited by 5
Deep learning for ψ -weakly dependent processes
Kengne, W., & Wade, M. (2024) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2024.106163 · Cited by 5
Implementing Image Processing and Deep Learning Techniques to Analyze Skin Cancer Images
Mushtaq, S., & Singh, O. (2024) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/150188 · Cited by 5
MgMViT: Multi-Granularity and Multi-Scale Vision Transformer for Efficient Action Recognition
Huo, H., & Li, B. (2024) · Electronics · doi:10.3390/electronics13050948 · Cited by 5
DSCU-Net: MEMS Defect Detection Using Dense Skip-Connection U-Net
Wu, S., Zhu, Y., & Liang, P. (2024) · Symmetry · doi:10.3390/sym16030300 · Cited by 5
Jauro, F., Gital, A. Y., Abdullahi, A. U., Abdulsalami, A. O., Abdullahi, M., Ibrahim, A. A., & Chiroma, H. (2024) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2024.200349 · Cited by 5
A Method for Image-Based Interpretation of the Pulverized Coal Cloud in the Blast Furnace Tuyeres
Zhou, G., Saxén, H., Mattila, O., et al. (2024) · Processes · doi:10.3390/pr12030529 · Cited by 5
Advancing Few-Shot Black-Box Attack With Alternating Training
Meng, L., Shao, M., Wang, F., Qiao, Y., & Xu, Z. (2024) · IEEE Transactions on Reliability · doi:10.1109/tr.2024.3369865 · Cited by 5
A joint autoencoder and classifier deep neural network for AD and MCI classification
Ganotra, R., Gupta, S., & Dora, S. (2024) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.23054 · Cited by 6
Hand gesture recognition with deep residual network using Semg signal
Khattak, A. S., Zain, A. B. M., hassan, R. B., Nazar, F., Haris, M., & Ahmed, B. A. (2024) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt-2023-0208 · Cited by 5
Regularization Effects in Deep Learning Architecture
Liman, M. D., Osanga, I. S., Alu, E. S., & Zakariya, S. (2024) · Journal of the Nigerian Society of Physical Sciences · doi:10.46481/jnsps.2024.1911 · Cited by 7
Leveraging a Two-Level Attention Mechanism for Deep Face Recognition with Siamese One-Shot Learning
Albayati, A. M., Chtourou, W., & Zarai, F. (2024) · Journal of Robotics and Control (JRC) · doi:10.18196/jrc.v5i1.20135 · Cited by 5
ℤ2 × ℤ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks
Dong, Z., Cara, M. C., Dahale, G. R., Forestano, R. T., Gleyzer, S. V., Justice, D., Kong, K., Magorsch, T., Matchev, K. T., Matcheva, K., et al. (2024) · Axioms · doi:10.3390/axioms13030188 · Cited by 5
Li, B., Lu, B., Lin, H., et al. (2024) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2024.3377357 · Cited by 5
Multi-Level Attention Split Network: A Novel Malaria Cell Detection Algorithm
Xiong, Z., & Wu, J. Y. (2024) · Information · doi:10.3390/info15030166 · Cited by 5
Online learning for DBC segmentation of new IGBT samples based on computed laminography imaging
Li, Y., Luo, M., Fei, X., Liu, S., & Wei, C. (2024) · Discover Applied Sciences · doi:10.1007/s42452-024-05807-3 · Cited by 6
PSE-Net: Channel pruning for Convolutional Neural Networks with parallel-subnets estimator
Wang, S., Xie, T., Liu, H., Zhang, X., & Cheng, J. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106263 · Cited by 5
He, Z., & Xu, W. (2024) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2024.2330702 · Cited by 5
RobustDiCE: Robust and Distributed CNN Inference at the Edge
Guo, X., Jiang, Q., Pimentel, A. D., & Stefanov, T. P. (2024) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/asp-dac58780.2024.10473970 · Cited by 6
Inverse Weight-Balancing for Deep Long-Tailed Learning
Dang, W., Yang, Z., Dong, W., Li, X., & Shi, G. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i10.29055 · Cited by 6
Hu, H., Gao, C., Li, Y., Deng, X., Lai, Y., Ma, C., Liu, Y., & Wang, H. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i3.27994 · Cited by 5
Wang, M., Yang, H., Huang, J., et al. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i19.30167 · Cited by 5
Regeneration Learning: A Learning Paradigm for Data Generation
Tan, X., Qin, T., Bian, J., Liu, T., & Bengio, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i20.30271 · Cited by 10
Yang, J., Yune, S., Lim, S., Kim, D., & Kim, J.-Y. (2024) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/asp-dac58780.2024.10473872 · Cited by 5
Cao, N., & Saukh, O. (2023) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads60453.2023.00084 · Cited by 8
Learning Accurate Pseudo-Labels via Feature Similarity in the Presence of Label Noise
Wang, P., Wang, X., Wang, Z., & Dong, Y. (2024) · Applied Sciences · doi:10.3390/app14072759 · Cited by 5
Anisotropic Green Tide Patch Information Extraction Based on Deformable Convolution
Cui, B., Liu, M., Chen, R., Zhang, H., & Zhang, X. (2024) · Remote Sensing · doi:10.3390/rs16071162 · Cited by 7
Fan, M., Cao, X., Lü, F., Xie, S., Yu, Z., Chen, Y., Lu, Z. Y., & Li, L. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad3889 · Cited by 5
Rehman, A. U., Mahmood, T., Alamri, F. S., Saba, T., & Naseem, S. (2024) · Microscopy Research and Technique · doi:10.1002/jemt.24557 · Cited by 5
Cao, Z. L., Wang, K., Wen, J., Li, C., Wu, Y., Wang, X., & Yu, W. (2024) · Frontiers in Zoology · doi:10.1186/s12983-024-00531-5 · Cited by 6
A rapid field crop data collection method for complexity cropping patterns using UAV and YOLOv3
Zhang, H., Wang, B., Tang, Z., Xue, J., Chen, R.-H., Kan, H.-P., Lu, S., Feng, L., He, Y., & Yi, S. (2024) · Frontiers in Earth Science · doi:10.1007/s11707-024-1109-y · Cited by 6
PHD-NAS: Preserving helpful data to promote Neural Architecture Search
Lu, S., Hu, Y. H., Yang, L., Mei, J., Sun, Z., Tan, J., & Song, C. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127646 · Cited by 5
Attention-Enhanced ResNet and feature Fusion for Chinese Herbal Classification
Zhang, C., Pan, X., & Jiang, Y. (2023) · doi:10.1145/3644116.3644316 · Cited by 5
Subeesh, A., & Chauhan, N. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.03.227 · Cited by 5
Ren, L., Yang, X., Huang, S., Zhong, Z., Peng, J., Ye, L., Hou, Y., & Zhang, B. (2024) · Optics Express · doi:10.1364/oe.521432 · Cited by 5
AI for brain-computer interfaces
Haslacher, D., Akmazoglu, T. B., van Beinum, A., Starke, G., Buthut, M., & Soekadar, S. R. (2024) · Developments in neuroethics and bioethics · doi:10.1016/bs.dnb.2024.02.003 · Cited by 8
Malik, N., & Malik, S. K. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03132-1 · Cited by 6
On Mobile Pose Estimation and Action Recognition Design and Implementation
Aslanyan, M. (2024) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661824010036 · Cited by 6
A Comparative Analysis of Oak Wood Defect Detection Using Two Deep Learning (DL)-Based Software
Jambreković, B., Veselčić, F., Ištok, I., Sinković, T., Živković, V., & Sedlar, T. (2024) · Applied System Innovation · doi:10.3390/asi7020030 · Cited by 5
Rolling Bearing Fault Diagnosis with Time-Frequency Image Based on Deep Learning
Zhao, Z., Gao, X., Wang, H., & Tian, J. R. (2023) · Global Reliability and Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-hangzhou58797.2023.10482564 · Cited by 5
An Approach for Human Posture Recognition Based on the Fusion PSE-CNN-BiGRU Model
Cao, X., Wang, X., Geng, X., Wu, D., & An, H. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.046752 · Cited by 6
Saha, P., De, A., Roy, S. D., & Bhowmik, M. K. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18953-y · Cited by 6
A Deep Learning Model Using VGG-16 and Neural Networks to Identify Pneumonia from Chest X-Ray Images
Kirankumarhumse, Harshitha, R., Parameshachari, B. D., et al. (2024) · International Conference on Integrated Circuits and Communication Systems (ICICACS) · doi:10.1109/icicacs60521.2024.10498410 · Cited by 5
A novel artificial visual system for motion direction detection in color images
Tao, S., Zhang, Z., Zhao, R., Tang, Z., & Todo, Y. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111816 · Cited by 5
Zhao, H., Zhao, Y., Tomko, M., & Khoshelham, K. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3388965 · Cited by 6
CuPBoP: Making CUDA a Portable Language
Han, R., Chen, J., Garg, B., et al. (2024) · ACM Transactions on Design Automation of Electronic Systems · doi:10.1145/3659949 · Cited by 5
Attention-driven residual-dense network for no-reference image quality assessment
Zhang, Y., Song, Y., Lv, X., Shao, M., & Leng, Q. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03172-7 · Cited by 5
How Artificial Inelegance Is Transforming Aesthetic Dentistry: A Review
Hamdy, T. M. (2024) · Current Oral Health Reports · doi:10.1007/s40496-024-00372-5 · Cited by 5
Multi-scale 3D-CRU for EEG emotion recognition*
Dong, H., Zhou, J., Fan, C., Zheng, W., Tao, L., & Kwan, H. K. (2024) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ad43f1 · Cited by 5
Lati̇foğlu, F., Zhusupova, A., İNCE, M. N., Ertürk, N. A., Özdet, B., İçer, S., Güven, A., Avşaroğulları, Ö. L., Keleşoğlu, Ş., & Kalay, N. (2024) · The European Journal of Research and Development · doi:10.56038/ejrnd.v4i1.421 · Cited by 5
Zhang, S., Liu, J., Michael, G., Zhu, K., Lei, D., Zhang, J., Liu, J., & Ren, M. (2024) · Remote Sensing · doi:10.3390/rs16091602 · Cited by 6
Fang, M., Wang, Y., Yang, L., Wu, H., Yin, Z., Liu, X., Xie, Z., & Kong, Z. (2024) · Electronics · doi:10.3390/electronics13091711 · Cited by 6
Chen, X., Luo, X., Wu, Z., et al. (2024) · Acta Oceanologica Sinica · doi:10.1007/s13131-023-2203-9 · Cited by 5
Latent Representation Learning for Geospatial Entities
Lee, W. J., & Lauw, H. W. (2024) · ACM Transactions on Spatial Algorithms and Systems · doi:10.1145/3663474 · Cited by 5
DC Corona Discharge Mode Identification Based on the Visible Light Images via the YOLOv8
Uckol, H. I., & İlhan, S. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3396833 · Cited by 7
Abdullah, A. B., Jawahar, M., Nalini, M., et al. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150455 · Cited by 5
Classification of Thoracic Abnormalities from Chest X-Ray Images with Deep Learning
Usman, N., Ashraf, M. U., Iqbal, M. J., Asaf, M. Z., Mir, M. M., Raza, U. A., & Sharif, B. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150402 · Cited by 5
A More Efficient Algorithm for Small Target Detection in Unmanned Aerial Vehicles
Zhang, Y., Dong, D., Liu, H., Liu, L., Deng, L., Gu, J. J., & Li, S. (2024) · IEEJ Transactions on Electrical and Electronic Engineering · doi:10.1002/tee.24096 · Cited by 6
Double reuses based residual network
Liu, Q., & Zhong, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127803 · Cited by 5
Deep Feature Retention Module Network for Texture Classification
Park, S.-H., Ahn, S.-Y., & Lee, S.-W. (2024) · Applied Sciences · doi:10.3390/app14104011 · Cited by 9
A Review of Explainable Fashion Compatibility Modeling Methods
Selwon, K., & Szymański, J. (2024) · ACM Computing Surveys · doi:10.1145/3664614 · Cited by 7
Deep Learning‐Assisted Design of Bilayer Nanowire Gratings for High‐Performance MWIR Polarizers
Lee, J., Oh, J., Chi, H., Lee, M.-S., Hwang, J., Jeong, S., Kang, S., Jee, H., Bae, H., Hyun, J., et al. (2024) · Advanced Materials Technologies · doi:10.1002/admt.202302176 · Cited by 6
Bi, Y., Pan, Y., & Li, G. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3400519 · Cited by 5
Vision-based LCD/OLED defect detection methods： a critical summary
Siyuan, L., & Yiquan, W. (2024) · Journal of Image and Graphics · doi:10.11834/jig.230518 · Cited by 5
Deep Learning Models for Software Defect Classification
Singh, K., Aggarwal, A., Aneja, R. D., et al. (2024) · International Conference on Device Intelligence, Computing and Communication Technologies (DICCT) · doi:10.1109/dicct61038.2024.10532830 · Cited by 5
Deep contrastive learning based hybrid network for Typhoon intensity classification
Yin, P., Fang, Y., Chen, H., et al. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124229 · Cited by 5
Research on Road Pattern Recognition of a Vision-Guided Robot Based on Improved-YOLOv8
Zhang, X., & Yang, Y. (2024) · Applied Sciences · doi:10.3390/app14114424 · Cited by 5
Liu, Z., Fu, R., Wu, N., et al. (2024) · Atmospheric Research · doi:10.1016/j.atmosres.2024.107505 · Cited by 5
Autoencoding Labeled Interpolator, Inferring Parameters from Image and Image from Parameters
SaraerToosi, A., & Broderick, A. E. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad3e76 · Cited by 5
A Hybrid ROI Extraction Approach for Mask and Unmask Facial Recognition System using Light-CNN
Ahmed, A. A., & Alghareb, F. S. (2024) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/160190 · Cited by 5
Nguyen, P., Sooriyaarachchi, J., Huang, Q., & Baker, C. L. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012127 · Cited by 5
A Novel Rock Mass Discontinuity Detection Approach with CNNs and Multi-View Image Augmentation
Yalcin, I., Can, R., Gökçeoğlu, C., & Kocaman, S. (2024) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi13060185 · Cited by 7
Altıntaş, M., & Özıç, M. Ü. (2024) · Konya Journal of Engineering Sciences · doi:10.36306/konjes.1346134 · Cited by 5
Haque, A., & Rao, K. S. (2024) · International Journal of Speech Technology · doi:10.1007/s10772-024-10109-5 · Cited by 8
A framework for training larger networks for deep Reinforcement learning
Ota, K., Jha, D. K., & Kanezaki, A. (2024) · Machine Learning · doi:10.1007/s10994-024-06547-6 · Cited by 48
A Deep Learning Approach for Classification of Vitiligo and Scar Images
Rajesh, E., Madanagopal, C., John, R., et al. (2024) · doi:10.1109/iccsp60870.2024.10543467 · Cited by 5
Artificial Intelligence in Cancer Diagnosis: A Game-changer in Healthcare
Sahoo, P., Kundu, M., & Begum, J. (2024) · Current Pharmaceutical Biotechnology · doi:10.2174/0113892010298852240528123911 · Cited by 10
An Efficient Model on AIoT Devices for Trash Classification Applications
Vo, N.-S., Nguyen, N.-T.-X., Le, G.-P., Nguyen, L.-T.-N., & Pham, H.-A. (2024) · SN Computer Science · doi:10.1007/s42979-024-02988-x · Cited by 5
Zhu, M., Guo, Y., & Song, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124486 · Cited by 6
RAD-BNN: Regulating activation distribution for accurate binary neural network
Yuan, M., & Pei, S. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105114 · Cited by 5
Application Status and Prospect of Deep Learning in Echocardiography
Qi, Q., Han, X., & Liu, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3413681 · Cited by 5
ViT-LOB: Efficient Vision Transformer for StockPrice Trend Prediction Using Limit Order Books
Liu, Z., Sham, C., Ma, L., et al. (2024) · International Conference on Applied System Innovation · doi:10.1109/icasi60819.2024.10547868 · Cited by 5
Aresta, G., Araújo, T., Reiter, G. S., Mai, J., Riedl, S., Grechenig, C., Guymer, R. H., Wu, Z., Schmidt-Erfurth, U. M., & Bogunović, H. (2024) · Translational Vision Science & Technology · doi:10.1167/tvst.13.6.7 · Cited by 6
Maturity grading of jujube for industrial applications harnessing deep learning
Mahmood, A., Tiwari, A. K., Singh, S. K., et al. (2024) · Engineering Computations · doi:10.1108/ec-08-2023-0426 · Cited by 5
Novel Perceptual Mach Band-Based Deep Attention Network for Cyclone Intensity Estimation
Bansal, S., Puhan, N. B., & Pattnaik, S. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3415776 · Cited by 5
Scaling in Deep and Shallow Learning Architectures
Koresh, E., Halevi, T., Meir, Y., Dilmoney, D., Dror, T., Gross, R. D., Tevet, O., Hodassman, S., & Kanter, I. (2024) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2024.129909 · Cited by 5
How to Improve Neural Network Training Using Evolutionary Algorithms
Carvalho, P., Lourenço, N., & Machado, P. (2024) · SN Computer Science · doi:10.1007/s42979-024-02972-5 · Cited by 6
Chen, X., Xia, J., Pang, J., et al. (2024) · Computers & Geosciences · doi:10.1016/j.cageo.2024.105663 · Cited by 5
Identifying voices using convolution neural network models AlexNet and ResNet
Luluh, A. A., & Muniasamy, A. (2024) · Computing and artificial intelligence. · doi:10.59400/cai.v2i1.441 · Cited by 5
Prediction of geothermal temperature field by multi-attribute neural network
Gao, W., & Zhao, J. (2024) · Geothermal Energy · doi:10.1186/s40517-024-00300-x · Cited by 5
Impact of Color Space and Color Resolution on Vehicle Recognition Models
Ghanem, S., & Holliman, J. H. (2024) · Journal of Imaging · doi:10.3390/jimaging10070155 · Cited by 5
Semantic Redundancy-Aware Multi-View Edge Inference Based on Rate-Distortion in IoT Systems
Liu, Z., Guo, C., & Zhu, M. (2024) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc57260.2024.10571204 · Cited by 5
Bensalah, N., Ayad, H., Adib, A., & Farouk, A. I. E. (2024) · International Journal of Intelligent Computing and Cybernetics · doi:10.1108/ijicc-03-2024-0106 · Cited by 5
CLRiuS: Contrastive Learning for intrinsically unordered Steel Scrap
Schäfer, M., Faltings, U., & Glaser, B. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100573 · Cited by 5
Alhayan, F., Himdi, H., & Alharbi, B. (2024) · IEEE Access · doi:10.1109/access.2024.3424531 · Cited by 7
Liu, Y., Wang, X., Yuan, C., et al. (2024) · International Journal of Digital Earth · doi:10.1080/17538947.2024.2368705 · Cited by 5
Ahmadi, A., Courtney, M., Ren, C. L., & Ingalls, B. (2024) · Microbiology Spectrum · doi:10.1128/spectrum.00032-24 · Cited by 6
A Privacy-Preserving Prediction Model for Individualized Electric Vehicle Energy Consumption
Hu, X., & Sikdar, B. (2024) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2024.3427056 · Cited by 5
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
