Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Yao, Yinghua"'
Publikováno v:
Machine Learning 2024
Recent advancements in the realm of deep generative models focus on generating samples that satisfy multiple desired properties. However, prevalent approaches optimize these property functions independently, thus omitting the trade-offs among them. I
Externí odkaz:
http://arxiv.org/abs/2407.04493
Real-world datasets inevitably contain biases that arise from different sources or conditions during data collection. Consequently, such inconsistency itself acts as a confounding factor that disturbs the cluster analysis. Existing methods eliminate
Externí odkaz:
http://arxiv.org/abs/2311.01252
Many machine learning applications encounter a situation where model providers are required to further refine the previously trained model so as to gratify the specific need of local users. This problem is reduced to the standard model tuning paradig
Externí odkaz:
http://arxiv.org/abs/2304.14831
Deep learning models can be fooled by small $l_p$-norm adversarial perturbations and natural perturbations in terms of attributes. Although the robustness against each perturbation has been explored, it remains a challenge to address the robustness a
Externí odkaz:
http://arxiv.org/abs/2304.03955
Relative attribute (RA), referring to the preference over two images on the strength of a specific attribute, can enable fine-grained image-to-image translation due to its rich semantic information. Existing work based on RAs however failed to reconc
Externí odkaz:
http://arxiv.org/abs/2111.13411
This paper proposes Differential-Critic Generative Adversarial Network (DiCGAN) to learn the distribution of user-desired data when only partial instead of the entire dataset possesses the desired property. DiCGAN generates desired data that meets th
Externí odkaz:
http://arxiv.org/abs/2107.06700
Publikováno v:
Machine Learning; Jun2024, Vol. 113 Issue 6, p3711-3730, 20p
Akademický článek
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Autor:
Wang, Juan1 (AUTHOR), Yao, Yinghua2 (AUTHOR), Zhang, Yinggui2 (AUTHOR), Wang, Ximing3 (AUTHOR)
Publikováno v:
Computational Intelligence & Neuroscience. 8/21/2022, p1-11. 11p.
Akademický článek
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