Search Results for author: Bo-Wun Cheng

Found 5 papers, 4 papers with code

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

Rethinking Ensemble-Distillation for Semantic Segmentation Based Unsupervised Domain Adaptation

1 code implementation29 Apr 2021 Chen-Hao Chao, Bo-Wun Cheng, Chun-Yi Lee

Recent researches on unsupervised domain adaptation (UDA) have demonstrated that end-to-end ensemble learning frameworks serve as a compelling option for UDA tasks.

Ensemble Learning Semantic Segmentation +1

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

Semantic Segmentation Based Unsupervised Domain Adaptation via Pseudo-Label Fusion

no code implementations1 Jan 2021 Chen-Hao Chao, Bo-Wun Cheng, Chien Feng, Chun-Yi Lee

In this paper, we propose a pseudo label fusion framework (PLF), a learning framework developed to deal with the domain gap between a source domain and a target domain for performing semantic segmentation based UDA in the unseen target domain.

Pseudo Label Segmentation

On Investigating the Conservative Property of Score-Based Generative Models

1 code implementation26 Sep 2022 Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng, Chun-Yi Lee

In addition, we show that USBMs' inability to preserve the property of conservativeness may lead to degraded performance in practice.

Image Generation

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