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 56,401–56,500 of 108,439
Human Activity Recognition Based On Video Summarization And Deep Convolutional Neural Network
Kushwaha, A., Khare, M., Mounika, B., & Khare, A. (2024) · The Computer Journal · doi:10.1093/comjnl/bxae028 · Cited by 8
Brake Disc Deformation Detection Using Intuitive Feature Extraction and Machine Learning
Dózsa, T., Őri, P., Szabari, M. M., et al. (2024) · Machines · doi:10.3390/machines12040214 · Cited by 8
Entropy Induced Pruning Framework for Convolutional Neural Networks
Lu, Y., Guan, Z., Yang, Y., Zhao, W. Z., Gong, M., & Cai, X. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i4.28184 · Cited by 9
PMAC: Personalized Multi-Agent Communication
Meng, X., & Tan, Y. C. R. S. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i16.29700 · Cited by 11
Li, J., Cai, M., Kan, Z., & Xiao, S. (2024) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-024-09641-0 · Cited by 14
Wiseong, J., Min, K., Hu, X., Li, S., Wang, X., Bodong, S., & Li, C. (2024) · PLoS ONE · doi:10.1371/journal.pone.0294267 · Cited by 12
Tong, Y., Luo, X., Ma, L., Xie, S., Yang, W., & Guo, Y. (2024) · Pattern Analysis and Applications · doi:10.1007/s10044-024-01258-z · Cited by 8
Verma, K., Sikka, G., Swaraj, A., Kumar, S., & Kumar, A., V (2024) · SN Computer Science · doi:10.1007/s42979-024-02695-7 · Cited by 16
Wan, S., Lin, S. S., Yuan, Q., & He, Z. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03146-9 · Cited by 8
Residual Graph Convolutional Network for Bird’s-Eye-View Semantic Segmentation
Chen, Q., & Qi, X. (2024) · IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv57701.2024.00329 · Cited by 8
Jing, Z., Li, P., Wu, B., et al. (2024) · Remote Sensing · doi:10.3390/rs16081332 · Cited by 8
Sertkaya, M. E., Ergen, B., Türkoğlu, M., & Tonkal, Ö. (2024) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.23079 · Cited by 10
A MobileNetV2 model of transfer learning is employed for remote sensing image classification
Cao, L. (2024) · Advances in Engineering Technology Research · doi:10.56028/aetr.10.1.596.2024 · Cited by 8
Agricultural cyber-physical systems: evolution, basic, and fundamental concepts
Dhatterwal, J. S., Kaswan, K. S., & Chithaluru, P. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13185-1.00002-2 · Cited by 8
Yang, J., Lin, N., Zhang, K., Fu, C., & Zhang, C. (2024) · Energy · doi:10.1016/j.energy.2024.131414 · Cited by 9
VOC data-driven evaluation of vehicle cabin odor: from ANN to CNN-BiLSTM
Tian, D., Li, Q., Liu, F., Khan, J., Abbas, M. Q., & Du, Z. (2024) · Environmental Science and Pollution Research · doi:10.1007/s11356-024-33293-y · Cited by 10
Learning a complex network representation for shape classification
Ribas, L. C., & Bruno, O. M. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110566 · Cited by 8
Review on Detecting Pneumonia in Deep Learning
Sayeed, M. S., & Toh, J. J. (2024) · International Journal on Robotics Automation and Sciences · doi:10.33093/ijoras.2024.6.1.10 · Cited by 10
Zhao, Y., Wang, K., & Louri, A. (2024) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2024.3401543 · Cited by 8
AstroBug: Automatic Game Bug Detection Using Deep Learning
Azizi, E., & Zaman, L. (2024) · IEEE Transactions on Games · doi:10.1109/tg.2024.3402626 · Cited by 9
Valipoor, M. M., de Antonio, A., & Cabrera, J. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01350-8 · Cited by 16
Object Detection with Deep Learning
Bhadiyadra, Y. (2023) · International Journal of Science and Research (IJSR) · doi:10.21275/mr23717204529 · Cited by 8
Vivek, J., Sridharan, N. V., Mahanta, T. K., Sugumaran, V., Amarnath, M., Ramteke, S. M., & Marian, M. (2024) · Industrial Lubrication and Tribology · doi:10.1108/ilt-12-2023-0414 · Cited by 8
AM-MSFF: A Pest Recognition Network Based on Attention Mechanism and Multi-Scale Feature Fusion
Zhang, M., Yang, W., Chen, D., Fu, C., & Wei, F. (2024) · Entropy · doi:10.3390/e26050431 · Cited by 9
Zhu, S., Chen, G., Chen, H., Lu, Y., Wu, M., Zheng, B. R., Liu, D., Qian, C., & Chen, Y. (2024) · Medical Physics · doi:10.1002/mp.17171 · Cited by 12
Determining the quality level of ready to-eat stuffed mussels with Arduino-based electronic nose
Yavuzer, E., Köse, M., & Uslu, H. (2024) · Journal of Food Measurement & Characterization · doi:10.1007/s11694-024-02593-9 · Cited by 8
Yao, C., Su, C., Zou, J., et al. (2024) · Journal of Chemometrics · doi:10.1002/cem.3559 · Cited by 8
Efficient Deep Spiking Multilayer Perceptrons With Multiplication-Free Inference
Li, B., Leng, L., Shen, S., Zhang, K., Zhang, J., Liao, J., & Cheng, R. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3394837 · Cited by 9
Liu, C., & Su, H.-C. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102604 · Cited by 9
Drop-Connect as a Fault-Tolerance Approach for RRAM-based Deep Neural Network Accelerators
Xiang, M., Xie, X., Savarese, P., Yuan, X., Maire, M., & Li, Y. (2024) · IEEE VLSI Test Symposium (VTS) · doi:10.1109/vts60656.2024.10538531 · Cited by 10
Robust Cross-Modal Remote Sensing Image Retrieval via Maximal Correlation Augmentation
Wang, Z., Wang, X., Li, G., & Li, C. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3406606 · Cited by 9
A Multiscale Dual Attention Sparse Dual Network for Time-Frequency Image Denoising of Radar Signal
Li, S., Du, X., Cui, G., Chen, X., & Zheng, J. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3403856 · Cited by 8
Vu, T. P., Luong, V. S., & Le, M. (2024) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2024.2360052 · Cited by 9
DCP-Net: An Efficient Image Segmentation Model for Forest Wildfires
Qiao, L., Yuan, W. M., & Liu, T. (2024) · Forests · doi:10.3390/f15060947 · Cited by 8
EGST: An Efficient Solution for Human Gaits Recognition Using Neuromorphic Vision Sensor
Chen, L., Zhang, Z., Xiao, Y., & Wang, Y. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3409167 · Cited by 9
FDT: Improving the transferability of adversarial examples with frequency domain transformation
Ling, J., Chen, J., & Li, H. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103942 · Cited by 8
Pecháč, M., Chovanec, M., & Farkaš, I. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128033 · Cited by 8
Yan, K., Li, L., Cheng, R., et al. (2024) · Optics Express · doi:10.1364/oe.527211 · Cited by 8
Deep image clustering: A survey
Huang, H., Wang, C., Wei, X., & Zhou, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128101 · Cited by 12
Global-local graph neural networks for node-classification
Eliasof, M., & Treister, E. (2024) · Pattern Recognition Letters · doi:10.1016/j.patrec.2024.06.013 · Cited by 8
Rey, A. (2024) · L’Année psychologique · doi:10.3917/anpsy1.242.0165 · Cited by 7
Optimizing Mobile Vision Transformers for Land Cover Classification
Rozario, P. F., Gadgil, R., Lee, J., Gomes, R., Keller, P., Liu, Y., Sipos, G., McDonnell, G., Impola, W., & Rudolph, J. (2024) · Applied Sciences · doi:10.3390/app14135920 · Cited by 11
An Analysis of Traditional Methods and Deep Learning Methods in SSVEP-Based BCI: A Survey
Wu, J., & Wang, J. (2024) · Electronics · doi:10.3390/electronics13142767 · Cited by 12
Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review
Faiz, M., Mounika, B. G., Akbar, M., et al. (2024) · ADCAIJ ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL · doi:10.14201/adcaij.31420 · Cited by 8
A review of emerging trends in photonic deep learning accelerators
Atwany, M., Pardo, S., Serunjogi, S. M., & Rasras, M. (2024) · Frontiers in Physics · doi:10.3389/fphy.2024.1369099 · Cited by 11
Artificial Intelligence-Driven Framework for Augmented Reality Markerless Navigation in Knee Surgery
Hu, X. L., Cutolo, F., Iqbal, H., Henckel, J., & y Baena, F. R. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3429048 · Cited by 10
Graph isomorphism network with weighted multi‐aggregators for building shape classification
Zhang, Y., Liu, J., Wang, Y., et al. (2024) · Transactions in GIS · doi:10.1111/tgis.13201 · Cited by 8
Heterogeneous Network-Based Contrastive Learning Method for PolSAR Land Cover Classification
Cai, J., Ma, Y., Feng, Z., Yang, S., & Jiao, L. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3429538 · Cited by 10
Real-Time Detection of Face Mask Usage Using Convolutional Neural Networks
Kanavos, A., Papadimitriou, O., Al-Hussaeni, K., Μaragoudakis, M., & Karamitsos, I. (2024) · Computers · doi:10.3390/computers13070182 · Cited by 8
Artificial Intelligence Empowering Medical Image Processing
Geroski, T., & Filipović, N. (2024) · Springer eBooks · doi:10.1007/978-3-031-60044-9_7 · Cited by 9
Wang, Q., Dong, H., Huang, Y., Liu, Z., & Gou, Y. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.052775 · Cited by 11
Ensemble Learning for Accurate Prediction of Heart Sounds using Gammatonegram Images
Singh, S. A., Singh, S. A., & Singh, A. D. (2024) · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · doi:10.55730/1300-0632.4087 · Cited by 8
Forest Wildfire Detection from Images Captured by Drones Using Window Transformer without Shift
Yuan, W. M., Qiao, L., & Liu, T. (2024) · Forests · doi:10.3390/f15081337 · Cited by 11
Cascaded UNet for progressive noise residual prediction for structure-preserving video denoising
Pimpale, A. M., & Bhurchandi, K. M. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104103 · Cited by 7
A self-attention armed optronic transformer in imaging through scattering media
Huang, Z., Shi, M., Ma, J., Gao, Y., & Liu, X. (2024) · Optics Communications · doi:10.1016/j.optcom.2024.130943 · Cited by 8
Feature pyramid biLSTM: Using smartphone sensors for transportation mode detection
Tang, Q., & Cheng, H. (2024) · Transportation Research Interdisciplinary Perspectives · doi:10.1016/j.trip.2024.101181 · Cited by 8
Open-Vocabulary Affordance Detection using Knowledge Distillation and Text-Point Correlation
Vo, T. V., Vu, M. N., Huang, B., Nguyen, T. Q., Le, N., Vo, T., & Nguyen, A. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610247 · Cited by 12
Foam detection in a stirred tank using deep learning neural networks
Hoseini, S. S., Rundquist, E., Poux, M., & Aubin, J. (2024) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2024.08.005 · Cited by 8
Mobsite, S., Alaoui, N., Boulmalf, M., & Ghogho, M. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109069 · Cited by 8
Lo, C., & Lin, T. C. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02474-4 · Cited by 9
Yao, J., Wu, Y., Liu, J., et al. (2024) · Applied Sciences · doi:10.3390/app14167078 · Cited by 8
Efficient, Scalable, and Sustainable DNN Training on SoC-Clustered Edge Servers
Xu, M., Xu, D., Lou, C., Zhang, L. L., Huang, G., Jin, X., & Liu, X. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3442430 · Cited by 8
Denoising Raman Spectra Using Autoencoder for Improved Analysis of Contamination in HDD
Gulyanon, S., Deepaisarn, S., Chokphantavee, S., Chokphantavee, S., Prathipasen, P., Laitrakun, S., Opaprakasit, P., Viriyavit, W., Jaikaew, N., Jindakaew, J., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3441824 · Cited by 9
Moniri, S., Schlosser, T., & Kowerko, D. (2024) · Computers · doi:10.3390/computers13080212 · Cited by 9
Real-Time Monitoring Method for Traffic Surveillance Scenarios Based on Enhanced YOLOv7
Yu, D., Yuan, Z., Wu, X., Wang, Y., & Liu, X. (2024) · Applied Sciences · doi:10.3390/app14167383 · Cited by 10
Ergin, F., Parlak, İ. B., Adel, M., et al. (2024) · Electronics · doi:10.3390/electronics13173414 · Cited by 8
Predicting Protests and Riots in Urban Environments With Satellite Imagery and Deep Learning
Warnke, S., & Runfola, D. M. (2024) · Transactions in GIS · doi:10.1111/tgis.13236 · Cited by 10
Gogulamudi, N., Madhu, G., Apparao Naidu, G., et al. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.102820 · Cited by 8
A State-of-the-Art Survey of Deep Learning for Lumbar Spine Image Analysis: X-ray, CT, and MRI
Zhang, R. (2024) · AI medicine. · doi:10.53941/aim.2024.100003 · Cited by 10
Li, M., Dai, W., Wang, G., Wang, B., Chen, K., Gao, Y., & Amankwah, S. O. Y. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.104115 · Cited by 9
Zero-Shot Out-of-Distribution Detection with Outlier Label Exposure
Ding, C., & Pang, G. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650173 · Cited by 10
CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme Conditions
Hong, H., Zhou, Y., Shu, X., & Hu, X. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651346 · Cited by 8
Guo, Z., Hou, B., Guo, X., Wu, Z., Yang, C., Ren, B., & Jiao, L. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3458408 · Cited by 8
Oscillations in an artificial neural network convert competing inputs into a temporal code
Duecker, K., Idiart, M., van Gerven, M. A. J., & Jensen, O. B. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012429 · Cited by 8
Xu, Y., Du, M., Xu, P., Liang, B., & Wang, H. (2024) · Physics Letters B · doi:10.1016/j.physletb.2024.139016 · Cited by 13
Guo, T., Luan, J., Gao, J., et al. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125414 · Cited by 8
Object Recognition as Next Token Prediction
Yue, K., Chen, B.-C., Geiping, J., Li, H., Goldstein, T., & Lim, S.-N. (2024) · 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/cvpr52733.2024.01575 · Cited by 11
Zou, Y., Zhao, W., Liu, T., et al. (2024) · Applied Sciences · doi:10.3390/app14198666 · Cited by 8
Zhao, B., Qin, Z., Wu, Y., Song, Y., Yu, H., & Gao, L. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3483749 · Cited by 8
Advanced CNN Architectures for Improved Garbage Image Classification: An In-depth Evaluation
Kanavos, A., Papadimitriou, O., Vonitsanos, G., et al. (2024) · South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM) · doi:10.1109/seeda-cecnsm63478.2024.00024 · Cited by 8
Exploring Bioinformatics Solutions for Improved Leishmaniasis Diagnostic Tools: A Review
Costa, N. T. C., Pereira, A. M. D. S., da Silva, C. M., Souza, E., Ferreira, L. F., de Oliveira, B., Hernandes, M. Z., & Pereira, V. R. A. (2024) · Molecules · doi:10.3390/molecules29225259 · Cited by 10
Impact of SAR Image Quantization Method on Target Recognition With Neural Networks
Kangwei, L., Wang, D., & An, D. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3495705 · Cited by 8
Giveki, D. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02518-9 · Cited by 8
Chen, L., Xie, C., Ma, D., et al. (2024) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2024.130244 · Cited by 8
Deng, Y.-X., Wang, J.-Y., Ko, C., Huang, C., Tsai, C., & Fu, L. (2024) · BioData Mining · doi:10.1186/s13040-024-00407-8 · Cited by 11
Identification of broken rice rate based on grading and morphological classification
Ye, J., Hu, Z., Chen, Y., Fu, D., & Zhang, J. (2024) · LWT · doi:10.1016/j.lwt.2024.117175 · Cited by 12
Bu, S., Jin, M., Wang, J., Xie, Y., & Zhang, L. (2024) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2024.3514297 · Cited by 9
Automatic Detection and Classification of Aurora in THEMIS All‐Sky Images
Johnson, J. W., Ozturk, D. S., Hampton, D. L., Connor, H. K., Blandin, M., & Keesee, A. M. (2024) · Journal of Geophysical Research Machine Learning and Computation · doi:10.1029/2024jh000292 · Cited by 8
Das, S., Kumari, R., & Singh, R. K. (2024) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-024-02645-9 · Cited by 10
Cross-Modal Guided Visual Representation Learning for Social Image Retrieval
Guan, Z., Zhao, W., Liu, H., Nakashima, Y., Babaguchi, N., & He, X. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3519112 · Cited by 8
Zhou, B., Li, C., & Andrade, J. E. (2024) · Géotechnique · doi:10.1680/jgeot.24.01270 · Cited by 8
Anatomic Boundary‐Aware Explanation for Convolutional Neural Networks in Diagnostic Radiology
Yuan, H. (2024) · iRadiology · doi:10.1002/ird3.113 · Cited by 8
Deep Learning-Based Diagnosis Algorithm for Alzheimer’s Disease
Jin, Z., Gong, J., Deng, M., et al. (2024) · Journal of Imaging · doi:10.3390/jimaging10120333 · Cited by 8
Maximizing Uncertainty for Federated Learning via Bayesian Optimization-Based Model Poisoning
Aristodemou, M., Liu, X., Wang, Y., Kyriakopoulos, K. G., Lambotharan, S., & Wei, Q. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3531143 · Cited by 8
Qureshi, I. (2025) · AI · doi:10.3390/ai6020028 · Cited by 12
Zhan, Y., Yıldırım, A., Boninsegna, L., & Alber, F. (2025) · Genome biology · doi:10.1186/s13059-024-03472-8 · Cited by 9
Lei, P., Wang, C., & Liu, P. (2025) · Applied Sciences · doi:10.3390/app15042039 · Cited by 13
Ullah, A., Numan, M., Khalid, M. N. A., & Majid, A. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129767 · Cited by 10
Ultrasonic detection and deep learning for high-precision concrete strength prediction
Gan, X., Wang, W., Jiang, C., Ye, L., Chen, F., Zhou, T., & Zhao, Y. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.112372 · Cited by 8
Iqbal, M., Haider, S. T., Shoukat, R. S., Rehman, S. U., Mahmood, K., Villar, S. G., López, L. A. D., & Ashraf, I. (2025) · Journal of Food Quality · doi:10.1155/jfq/8522400 · 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
