Roy Tseng — accepted AI-conference papers

2024

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

Li-Yuan Tsao, Yi-Chen Lo, Chia-Che Chang, Hao-Wei Chen, Roy Tseng, Chien Feng +1

CVPR 2024poster

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 suboptimal results due to a fixed sampling temperature. To ov…

Cited by 5 Source ↗PDF ↗code ↗Scholar ↗

2023

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

Jie-En Yao, Li-Yuan Tsao, Yi-Chen Lo, Roy Tseng, Chia-Che Chang, Chun-Yi Lee

CVPR 2023poster

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 predefined fixed-scale SR, limiting their potential in r…

Cited by 40 Source ↗PDF ↗code ↗Scholar ↗