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pro vyhledávání: '"Chang Chia-Che"'
Autor:
Shen, Mu-Yi, Hsu, Chia-Chi, Hou, Hao-Yu, Huang, Yu-Chen, Sun, Wei-Fang, Chang, Chia-Che, Liu, Yu-Lun, Lee, Chun-Yi
In this study, we introduce the DriveEnv-NeRF framework, which leverages Neural Radiance Fields (NeRF) to enable the validation and faithful forecasting of the efficacy of autonomous driving agents in a targeted real-world scene. Standard simulator-b
Externí odkaz:
http://arxiv.org/abs/2403.15791
Autor:
Tsao, Li-Yuan, Lo, Yi-Chen, Chang, Chia-Che, Chen, Hao-Wei, Tseng, Roy, Feng, Chien, Lee, Chun-Yi
Flow-based super-resolution (SR) models have demonstrated astonishing capabilities in generating high-quality images. However, these methods encounter several challenges during image generation, such as grid artifacts, exploding inverses, and subopti
Externí odkaz:
http://arxiv.org/abs/2403.10988
Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with the normalizing flow. However, these methods can only perform a predef
Externí odkaz:
http://arxiv.org/abs/2303.05156
Autor:
Liao, Ting-Hsuan, Liao, Huang-Ru, Yang, Shan-Ya, Yao, Jie-En, Tsao, Li-Yuan, Liu, Hsu-Shen, Cheng, Bo-Wun, Chao, Chen-Hao, Chang, Chia-Che, Lo, Yi-Chen, Lee, Chun-Yi
Many unsupervised domain adaptation (UDA) methods have been proposed to bridge the domain gap by utilizing domain invariant information. Most approaches have chosen depth as such information and achieved remarkable success. Despite their effectivenes
Externí odkaz:
http://arxiv.org/abs/2211.08888
Autor:
Chao, Chen-Hao, Sun, Wei-Fang, Cheng, Bo-Wun, Lo, Yi-Chen, Chang, Chia-Che, Liu, Yu-Lun, Chang, Yu-Lin, Chen, Chia-Ping, Lee, Chun-Yi
Many existing conditional score-based data generation methods utilize Bayes' theorem to decompose the gradients of a log posterior density into a mixture of scores. These methods facilitate the training procedure of conditional score models, as a mix
Externí odkaz:
http://arxiv.org/abs/2203.14206
Publikováno v:
In Journal of Infection and Public Health October 2024 17(10)
Publikováno v:
In Pesticide Biochemistry and Physiology August 2024 203
Autor:
Lo, Yi-Chen, Chang, Chia-Che, Chiu, Hsuan-Chao, Huang, Yu-Hao, Chen, Chia-Ping, Chang, Yu-Lin, Jou, Kevin
In this paper, we present CLCC, a novel contrastive learning framework for color constancy. Contrastive learning has been applied for learning high-quality visual representations for image classification. One key aspect to yield useful representation
Externí odkaz:
http://arxiv.org/abs/2106.04989
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Akademický článek
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