Results 161 to 170 of about 6,306,959 (200)

Contrast-guided Virtual Monoenergetic Image Synthesis via Adversarial Learning for Coronary CT Angiography using Photon Counting Detector CT. [PDF]

Proc SPIE Int Soc Opt Eng
Chang S, Wilson M, Koons EK, Gong H, Hsieh SS, Yu L, McCollough CH, Leng S.   +7 more
europepmc   +1 more source

Dissecting Spatiotemporal Structures in Spatial Transcriptomics via Diffusion-Based Adversarial Learning. [PDF]

Research (Wash D C)
Wang H, Zhao J, Nie Q, Zheng C, Sun X.
europepmc   +1 more source

Shape-Aware Adversarial Learning for Scribble-Supervised Medical Image Segmentation with a MaskMix Siamese Network: A Case Study of Cardiac MRI Segmentation. [PDF]

Bioengineering (Basel)
Li C, Zheng Z, Wu D.
europepmc   +1 more source

Fault Diagnosis Method of Special Vehicle Bearing Based on Multi-Scale Feature Fusion and Transfer Adversarial Learning. [PDF]

Sensors (Basel)
Xiao Z, Xiao Z, Li D, Yang C, Chen W.
europepmc   +1 more source

Semi-supervised generative adversarial learning for denoising adaptive optics retinal images. [PDF]

Biomed Opt Express
Wang S, Li K, Yin Q, Ren J, Zhang J.
europepmc   +1 more source

EEG classification based on visual stimuli via adversarial learning. [PDF]

Cogn Neurodyn
Mishra R, Bhavsar A.
europepmc   +1 more source

Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning. [PDF]

J Am Stat Assoc
Shi C, Zhou Y, Li L.
europepmc   +1 more source

AI identifies potent inducers of breast cancer stem cell differentiation based on adversarial learning from gene expression data. [PDF]

Brief Bioinform
Li Z, Napolitano A, Fedele M, Gao X, Napolitano F.   +4 more
europepmc   +1 more source

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Adversarial Invariant Learning

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021
Though machine learning algorithms are able to achieve pattern recognition from the correlation between data and labels, the presence of spurious features in the data decreases the robustness of these learned relationships with respect to varied testing environments. This is known as out-of-distribution (OoD) generalization problem. Recently, invariant
Nanyang Ye 0001, Jingxuan Tang, Huayu Deng, Xiao-Yun Zhou 0001, Qianxiao Li, Zhenguo Li, Guang-Zhong Yang, Zhanxing Zhu   +7 more
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Learning Universal Adversarial Perturbation by Adversarial Example

Proceedings of the AAAI Conference on Artificial Intelligence, 2022
Deep learning models have shown to be susceptible to universal adversarial perturbation (UAP), which has aroused wide concerns in the community. Compared with the conventional adversarial attacks that generate adversarial samples at the instance level, UAP can fool the target model for different instances with only a single perturbation, enabling us to
Maosen Li, Yanhua Yang, Kun Wei, Xu Yang 0019, Heng Huang 0001   +4 more
openaire   +2 more sources