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 65,801–65,900 of 108,435
Plant Species Recognition Based on Hyperspectral Plant Canopy Images with Deep Learning
Chen, Y., Liu, K.-H., & Lin, C. (2023) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss52108.2023.10281644 · Cited by 5
Qi, Y., Wang, L., Zhao, C., Wang, N., & Chen, J. (2023) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss52108.2023.10283157 · Cited by 7
Explainable Automated Anomaly Recognition in Failure Analysis: is Deep Learning Doing it Correctly?
Arrighi, L., Barbon, S., Pellegrino, F. A., Simonato, M., & Zullich, M. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-44067-0_22 · Cited by 7
FerNeXt: Facial Expression Recognition Using ConvNeXt with Channel Attention
El‐Khashab, O., Hamdy, A. M., & Mahmoud, A. A. (2023) · International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) · doi:10.1109/miucc58832.2023.10278345 · Cited by 5
Graph-based Latent Space Traversal for New Molecules Discovery
Zhang, Y., Li, J., & Xu, C. (2023) · International Symposium on Visual Information Communication and Interaction (VINCI) · doi:10.1145/3615522.3615548 · Cited by 5
Few-Shot Learning via Task-Aware Discriminant Local Descriptors Network
Yan, L., Li, F., Zheng, X., & Zhang, L. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3614883 · Cited by 5
Hardware Acceleration of YOLOv5s Network Model Based on Aerospace-grade FPGA
Jiang, K., Zhou, H. L., Bian, C., et al. (2023) · Chinese Journal of Space Science · doi:10.11728/cjss2023.05.2022-0044 · Cited by 5
Wang, G.-A., Yan, X., Li, X., Liu, Y., Xia, J., & Zhu, X. (2023) · ACS Omega · doi:10.1021/acsomega.3c07086 · Cited by 5
Dong, Y., Li, X., Yang, Y., Wang, M., & Gao, B. (2023) · Bioengineering · doi:10.3390/bioengineering10111245 · Cited by 6
ADASSM: Adversarial Data Augmentation in Statistical Shape Models from Images
Karanam, M. S. T., Kataria, T., Iyer, K., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46914-5_8 · Cited by 5
Flash-based content addressable memory with L2 distance for memory-augmented neural network
Yang, H., Huang, P. C., Li, R., Tang, N., Zhang, Y., Zhou, Z., Liu, L., Liu, X., & Kang, J. (2023) · iScience · doi:10.1016/j.isci.2023.108371 · Cited by 5
Lazcano-Herrera, A. G., Fuentes-Aguilar, R. Q., Ramírez-Morales, A. O., & Alfaro-Ponce, M. (2023) · IEEE Access · doi:10.1109/access.2023.3328254 · Cited by 7
MetaFBP: Learning to Learn High-Order Predictor for Personalized Facial Beauty Prediction
Lin, L., Shen, Z., Yin, J.-L., Liu, Q., Yu, Y., & Chen, W. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612319 · Cited by 5
A Galaxy Morphology Classification Model Based on Momentum Contrastive Learning
Shen, G., Zou, Z., Luo, A.-L., Hong, S., & Kong, X. (2023) · Publications of the Astronomical Society of the Pacific · doi:10.1088/1538-3873/acf8f7 · Cited by 6
FAOT-Net: A 1.5-Stage Framework for 3D Pelvic Lymph Node Detection With Online Candidate Tuning
Zhang, Y. Y., Li, J., Li, X., et al. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3329464 · Cited by 5
Liu, Q., Nie, P., Dai, H., Ning, L., & Wang, J. (2023) · Applied Sciences · doi:10.3390/app132112082 · Cited by 5
Tree species classification on images from airborne mobile mapping using ML.NET
Michałowska, M., Rapiński, J., & Janicka, J. (2023) · European Journal of Remote Sensing · doi:10.1080/22797254.2023.2271651 · Cited by 5
Multi-modality frequency-aware cross attention network for fake news detection
Cui, W., Zhang, X., & Shang, M. (2023) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-233193 · Cited by 5
Sun, S., Jun, D., & Qin, X. (2023) · Aslib Journal of Information Management · doi:10.1108/ajim-05-2023-0180 · Cited by 5
Chuan, 金. J., & Changqing, 童. T. (2023) · Laser & Optoelectronics Progress · doi:10.3788/lop223154 · Cited by 5
Mortaji, S. T. H., & Shateri, S. (2023) · International Journal of Innovation in Engineering · doi:10.59615/ijie.3.3.1 · Cited by 15
Crafting Targeted Universal Adversarial Perturbations: Considering Images as Noise
Wang, H., Cai, D., Wang, L., & Xiong, Z. (2023) · IEEE Access · doi:10.1109/access.2023.3335094 · Cited by 6
WebDraw: A machine learning-driven tool for automatic website prototyping
Kaluarachchi, T., & Wickramasinghe, M. (2023) · Science of Computer Programming · doi:10.1016/j.scico.2023.103056 · Cited by 5
Chaka, K. T., Shiferaw, A. A., & Sharew, S. T. (2023) · Journal of Natural Fibers · doi:10.1080/15440478.2023.2286615 · Cited by 5
Girdhar, N., Coustaty, M., & Doucet, A. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8085-7_8 · Cited by 5
Video-Based Recognition of Aquatic Invasive Species Larvae Using Attention-LSTM Transformer
Chowdhury, S., Tisha, S. N., McGarrity, M. E., & Hamerly, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47969-4_18 · Cited by 5
Xu, T., Li, M., Wang, R., & Huang, R. (2023) · doi:10.1109/iccad57390.2023.10323672 · Cited by 13
Partial half fine-tuning for object detection with unmanned aerial vehicles
Pebrianto, W., Mudjirahardjo, P., & Pramono, S. H. (2023) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v13.i1.pp399-407 · Cited by 7
Qian, K., Wang, S., Zhang, S.-J., & Shen, J. (2023) · Sensors · doi:10.3390/s23239554 · Cited by 8
Zhang, Z., & Wang, Y. (2023) · ISA Transactions · doi:10.1016/j.isatra.2023.11.041 · Cited by 5
Homomorphic Encryption-Based Privacy Protection for Palmprint Recognition
Guo, Q., Shao, H., Liu, C., Wan, J., & Zhong, D. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8565-4_34 · Cited by 5
An ensemble of ResNet model for classification of Parkinson disease
Mahendran, M., & Visalakshi, R. (2023) · International journal of Nutrition Pharmacology Neurological Diseases · doi:10.4103/ijnpnd.ijnpnd_22_23 · Cited by 5
Multi-scale Feature Fusion Extraction Structure for the Leather Defect Detection Algorithm
He, X., Li, H., Huang, Z., et al. (2023) · IEEE International Conference on Pattern Recognition and Artificial Intelligence (PRAI) · doi:10.1109/prai59366.2023.10332000 · Cited by 5
WalkFormer: 3D mesh analysis via transformer on random walk
Guo, Q., He, F., Fan, B., Song, Y., Dai, J., & Fan, L. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09279-1 · Cited by 5
Zhao, W., Wang, S., Zhao, Y. F., Wei, J. J., Liang, Y., Lin, J., & Tang, Y. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107512 · Cited by 6
Ronkin, M., Akimova, E. N., Misilov, V. E., & Reshetnikov, K. (2023) · Bulletin of the South Ural State University Series Computational Mathematics and Software Engineering · doi:10.14529/cmse230401 · Cited by 6
Xinsen, L., Yang, K., Bingzhi, C., Xiuhong, C., Xinling, L., Xu, X., Chen, J., Ming, T., Pengtao, L., Zheng, X., et al. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3341101 · Cited by 5
Эпов, М. И., Danilovskiy, K. N., Nechaev, O. V., & Mikhaylov, I. V. (2023) · Russian Geology and Geophysics · doi:10.2113/rgg20234607 · Cited by 5
On-Board Real-Time Pedestrian Detection for Micro Unmanned Aerial Vehicles Based on YOLO-v8
Li, Z., Liu, Z., & Wang, X. (2023) · doi:10.1109/mlccim60412.2023.00042 · Cited by 5
Kuang, Y., Li, J., Liu, F., et al. (2023) · Journal of Optics · doi:10.1088/2040-8986/ad1589 · Cited by 5
DATASET AND IMPROVED YOLOV7 FOR TEXT-BASED TRAFFIC SIGN DETECTION
Chi, X., Huang, H., Yang, J., et al. (2023) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlviii-1-w2-2023-881-2023 · Cited by 5
Horry, M. J., Chakraborty, S., Pradhan, B., Paul, M., Zhu, J. F., Barua, P. D., Mir, H., Chen, F., Zhou, J. J., & Acharya, U. R. (2023) · IEEE Access · doi:10.1109/access.2023.3343451 · Cited by 5
Lin, T.-P., Yang, C., Liu, K., Huang, M., & Chen, Y. (2023) · Diagnostics · doi:10.3390/diagnostics13243685 · Cited by 6
Real-Time Detection of an Undercarriage Based on Receptive Field Blocks and Coordinate Attention
Gao, R., Ma, Y., Zhao, Z., Li, B., & Zhang, J. (2023) · Sensors · doi:10.3390/s23249861 · Cited by 5
Blood clot and fibrin recognition method for serum images based on deep learning
Hou, J., Ren, W., Zhao, W., et al. (2023) · Clinica Chimica Acta · doi:10.1016/j.cca.2023.117732 · Cited by 5
Liang, B., Wang, X., Zhao, W., et al. (2023) · Sensors · doi:10.3390/s24010012 · Cited by 5
A review of in-memory computing for machine learning: architectures, options
Snåšel, V., Dang, T. K., Kueng, J., & Kong, L. (2023) · International Journal of Web Information Systems · doi:10.1108/ijwis-08-2023-0131 · Cited by 6
Multi-Label Weighted Contrastive Cross-Modal Hashing
Yi, Z., Zhu, X., Wu, R., et al. (2023) · Applied Sciences · doi:10.3390/app14010093 · Cited by 5
From Segmentation to Classification: A Deep Learning Scheme for Sintered Surface Images Processing
Yang, Y. Q., Chen, T., & Zhao, L. (2023) · Processes · doi:10.3390/pr12010053 · Cited by 5
Kwon, T., Yoon, S.-S., Yoon, S.-S., et al. (2023) · Water · doi:10.3390/w16010097 · Cited by 5
Quantifying the Magnetic Interactions Governing Chiral Spin Textures Using Deep Neural Networks
Kong, J. F., Ren, Y., Tey, M. S. N., Ho, P., Khoo, K. H., Chen, X., & Soumyanarayanan, A. (2023) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.3c12655 · Cited by 5
CLOVER: Contrastive Learning for Onboard Vision-Enabled Robotics
Vincent, G., Ward, I. R., Moore, C., et al. (2023) · Journal of Spacecraft and Rockets · doi:10.2514/1.a35767 · Cited by 5
Using Deep Learning Techniques Furniture Image Classification
KILIÇ, K., Özcan, U., Kılıç, K., & Doğru, İ. A. (2023) · Journal of Polytechnic · doi:10.2339/politeknik.1315328 · Cited by 6
Barai, N. G., Banik, S., & Shamrat, F. M. J. M. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0141273 · Cited by 7
TOMATO PLANT DISEASE CLASSIFICATION USING LOCAL PATTERNS
Agarwal, M. (2023) · Advances in Electrical and Electronic Engineering · doi:10.15598/aeee.v21i4.5036 · Cited by 5
Automatic Inspection of Seal Integrity in Sterile Barrier Packaging: A Deep Learning Approach
Diaz, J. Z., Farooq, M. A., & Corcoran, P. (2024) · IEEE Access · doi:10.1109/access.2023.3348779 · Cited by 5
Diagnosis of Dental Caries in OCT Images Based on Deep Learning
Fan, S., Yu, H., Guan, Z., et al. (2023) · doi:10.1109/acp/poem59049.2023.10369830 · Cited by 5
Subdomain alignment based open-set domain adaptation image classification
Ji, K., Zhang, Q., & Zhu, S. (2024) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2024.104047 · Cited by 5
SW/SE-CNN: semi-wavelet and specific image edge extractor CNN for Gaussian image denoising
Esteki, S., & Naghsh‐Nilchi, A. R. (2024) · Neural Computing and Applications · doi:10.1007/s00521-023-09314-1 · Cited by 6
Quantizing separable convolution of MobileNets with mixed precision
Zhang, C., Zuo, G., Zheng, Z., Wu, Z., Rao, Y., & Jiang, Z. (2024) · Journal of Electronic Imaging · doi:10.1117/1.jei.33.1.013013 · Cited by 5
Efficient Deep Space Filling Curve
Chen, W., Yao, X., Zhang, X., & Yu, B. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01607 · Cited by 6
Automated Emotional Valence Estimation in Infants with Stochastic and Strided Temporal Sampling
Ning, M., Ertuğrul, I. Ö., Messinger, D. S., Cohn, J. F., & Salah, A. A. (2023) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii59096.2023.10388096 · Cited by 5
Keren-Rotem, T., Main, D. C., Barocas, A., et al. (2024) · Royal Society Open Science · doi:10.1098/rsos.231554 · Cited by 5
Brain Tumor Segmentation from MR Images using Customized U-net for a Smaller Dataset
Imtiaz, R., Mirza, M. W., Siddiq, A., et al. (2023) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas58349.2023.10389092 · Cited by 5
Wei, C. L., Abdul-Kadir, N. A., Mahmood, N. H., Embong, A. M., Harun, F. K. C., & Wahab, M. A. A. (2023) · IEEE International Biomedical Instrumentation and Technology Conference (IBITeC) · doi:10.1109/ibitec59006.2023.10390942 · Cited by 5
Nalla, V., Pouriyeh, S., Parizi, R. M., Trivedi, H., Sheng, Q. Z., Hwang, I., Seyyed-Kalantari, L., & Woo, M. (2024) · Current Problems in Diagnostic Radiology · doi:10.1067/j.cpradiol.2024.01.007 · Cited by 6
Self-supervised visual learning for analyzing firearms trafficking activities on the Web
Konstantakos, S., Chalkiadaki, D. I., Mademlis, I., Chrysochoou, A. A. R., & Papadopoulos, G. T. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386795 · Cited by 5
Ma, S., Yang, J. J., Chorzepa, M. G., Morris, C., Kim, S.-H. S., & Durham, S. A. (2024) · SN Computer Science · doi:10.1007/s42979-023-02569-4 · Cited by 5
Analysis and Modeling of Time Output Characteristics for Distributed Photovoltaic and Energy Storage
Liu, K., Chen, L., Dong, X., & Liu, L. (2024) · Energy Engineering · doi:10.32604/ee.2023.043658 · Cited by 5
Zhang, H., & Chung, A. C. S. (2024) · Bioengineering · doi:10.3390/bioengineering11010101 · Cited by 5
Tan, Z., Zhang, Q., Lan, G., Xu, J., Ou, C., An, L., Qin, J., & Huang, Y. (2024) · Mathematics · doi:10.3390/math12020347 · Cited by 6
An ASIC Accelerator for QNN With Variable Precision and Tunable Energy Efficiency
Wagle, A., Singh, G., Khatri, S. P., & Vrudhula, S. B. K. (2024) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2024.3357597 · Cited by 6
Detection of Acute Lymphoblastic Leukemia Using CollateNet
Jain, S., Vishnawat, P., Shukla, P. K., & Khatri, N. (2023) · International Conference on Technological Advancements in Computational Sciences (ICTACS) · doi:10.1109/ictacs59847.2023.10390121 · Cited by 5
Classification of Image with Convolutional Neural Network and TensorFlow on CIFAR-10 Dataset
Sharma, G., & Jadon, V. K. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-7077-3_51 · Cited by 7
Exploiting deep learning accelerators for neuromorphic workloads
Sun, P.-S. V., Titterton, A., Gopiani, A., et al. (2024) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ad2373 · Cited by 5
Concept-Based Anomaly Detection in Retail Stores for Automatic Correction Using Mobile Robots
Kapoor, A., Sengar, V., George, N., Vatsal, V., Gubbi, J., Balamuralidhar, P., & Pal, A. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10394209 · Cited by 5
Lei, S., Yu, S., Sun, J., Wang, Z., & Liao, Y. (2024) · Water · doi:10.3390/w16030427 · Cited by 6
Zheng, Y., Nydick, S. W., Huang, S.-J., & Zhang, S. (2024) · Educational Measurement: Issues and Practice · doi:10.1111/emip.12593 · Cited by 4
German CheXpert Chest X-ray Radiology Report Labeler
Wollek, A., Hyska, S., Sedlmeyr, T., Haitzer, P., Rueckel, J., Sabel, B. O., Ingrisch, M., & Lasser, T. (2024) · RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren · doi:10.1055/a-2234-8268 · Cited by 7
Learnable Tensor Graph Fusion Framework for Natural Image Segmentation
Zhang, X., Luo, C., Wang, X., Li, J., Zhao, S., & Jiang, D. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3360689 · Cited by 5
SWG: an architecture for sparse weight gradient computation
Wu, W., Tu, F., Li, X., et al. (2024) · Science China Information Sciences · doi:10.1007/s11432-022-3807-9 · Cited by 5
A New Bolts-Loosening Detection Method in High-Voltage Tower Based on Binocular Vision
Wang, Q., Zhang, H., Lin, C., et al. (2023) · International Conference on Mechatronics and Machine Vision in Practice (M2VIP) · doi:10.1109/m2vip58386.2023.10413415 · Cited by 5
Li, P., Zhong, N., Dong, W., et al. (2023) · International journal of agricultural and biological engineering · doi:10.25165/j.ijabe.20231606.6913 · Cited by 5
AdvMix: Adversarial Mixing Strategy for Unsupervised Domain Adaptive Object Detection
Chen, R., Lv, D., Dai, L., Jin, L., & Xiang, Z. (2024) · Electronics · doi:10.3390/electronics13040685 · Cited by 5
SLMSF-Net: A Semantic Localization and Multi-Scale Fusion Network for RGB-D Salient Object Detection
Peng, Y., Zhai, Z., & Feng, M. (2024) · Sensors · doi:10.3390/s24041117 · Cited by 5
Lu, Y., Tanaka, M., Kawakami, R., & Okutomi, M. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-0376-0_5 · Cited by 5
Gao, C., Fan, Q., Zhao, P., Sun, C., Dang, R., Feng, Y., Hu, B., & Wang, Q. (2024) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2024.124036 · Cited by 7
Quan, J., Ye, J., Lan, J., Wang, J., Hu, Z., Guo, Z., Wang, T., Han, Z., Wu, Z., Tan, T., et al. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106064 · Cited by 7
Glüsenkamp, T. (2020) · The European Physical Journal C · doi:10.1140/epjc/s10052-024-12473-7 · Cited by 5
Kumar, V. D. R., Muchina, F. E., Singh, P., & Nizami, T. K. (2024) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets59351.2024.10427364 · Cited by 5
S-Net: an S-shaped network for nodule detection in 3D CT images
Zhang, J., Zou, W., Hu, N., Zhang, B., & Wang, J. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad2b96 · Cited by 5
How technoscientific knowledge advances: A Bell-Labs-inspired architecture
Narayanamurti, V., & Tsao, J. Y. (2024) · Research Policy · doi:10.1016/j.respol.2024.104983 · Cited by 5
Polyp segmentation with interference filtering and dynamic uncertainty mining
Zhang, Y., Yang, G., Gong, C., et al. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad2b94 · Cited by 5
HFENet: Hybrid feature encoder network for detecting salient objects in RGB-thermal images
Sun, F., Zhou, W., Yan, W., & Zhang, Y. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104439 · Cited by 5
AI-enhanced Citizen Science Discovery of an Active Asteroid: (410590) 2008 GB140
Chandler, C. O., Sedaghat, N., Oldroyd, W. J., Trujillo, C. A., Burris, W. A., Hsieh, H. H., Kueny, J. K., Farrell, K. A., DeSpain, J. A., Magbanua, M. J. M., et al. (2024) · Research Notes of the AAS · doi:10.3847/2515-5172/ad2b67 · Cited by 5
NeuralDiffuser: Neuroscience-Inspired Diffusion Guidance for fMRI Visual Reconstruction
Li, H., Wu, H., & Chen, B. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3526051 · Cited by 9
Urban Visual Localization of Block-Wise Monocular Images with Google Street Views
Li, Z., Li, S., Anderson, J., et al. (2024) · Remote Sensing · doi:10.3390/rs16050801 · Cited by 5
Leaf disease detection using deep Convolutional Neural Networks
Hu, M., Long, S., Wang, C., & Wang, Z. (2024) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2711/1/012020 · Cited by 7
Synthetic Data for Sentinel-2 Semantic Segmentation
Clabaut, É., Foucher, S., Bouroubi, Y., & Germain, M. (2024) · Remote Sensing · doi:10.3390/rs16050818 · Cited by 5
Machine-Learning-Based Accessibility System
Banerjee, K., Singh, A., Akhtar, N., & Vats, I. (2024) · SN Computer Science · doi:10.1007/s42979-024-02615-9 · Cited by 7
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
