Search Results for author: Yi-Chen Lo

Found 6 papers, 6 papers with code

Boosting Flow-based Generative Super-Resolution Models via Learned Prior

1 code implementation16 Mar 2024 Li-Yuan Tsao, Yi-Chen Lo, Chia-Che Chang, Hao-Wei Chen, Roy Tseng, Chien Feng, Chun-Yi Lee

This prior is a latent code predicted by our proposed latent module conditioned on the low-resolution image, which is then transformed by the flow model into an SR image.

Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution

1 code implementation CVPR 2023 Jie-En Yao, Li-Yuan Tsao, Yi-Chen Lo, Roy Tseng, Chia-Che Chang, Chun-Yi Lee

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.

Ranked #3 on Image Super-Resolution on DIV2K val - 4x upscaling (using extra training data)

Image Super-Resolution

ELDA: Using Edges to Have an Edge on Semantic Segmentation Based UDA

1 code implementation16 Nov 2022 Ting-Hsuan Liao, Huang-Ru Liao, Shan-Ya Yang, Jie-En Yao, Li-Yuan Tsao, Hsu-Shen Liu, Bo-Wun Cheng, Chen-Hao Chao, Chia-Che Chang, Yi-Chen Lo, Chun-Yi Lee

Despite their effectiveness, using depth as domain invariant information in UDA tasks may lead to multiple issues, such as excessively high extraction costs and difficulties in achieving a reliable prediction quality.

Semantic Segmentation Synthetic-to-Real Translation +1

Denoising Likelihood Score Matching for Conditional Score-based Data Generation

2 code implementations ICLR 2022 Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng, Yi-Chen Lo, Chia-Che Chang, Yu-Lun Liu, Yu-Lin Chang, Chia-Ping Chen, Chun-Yi Lee

These methods facilitate the training procedure of conditional score models, as a mixture of scores can be separately estimated using a score model and a classifier.

Image Generation

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