Search Results for author: Chia-Che Chang

Found 8 papers, 5 papers with code

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

no code implementations9 Mar 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.

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

1 code implementation 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.

Denoising

COCO-GAN: Conditional Coordinate Generative Adversarial Network

no code implementations ICLR 2019 Chieh Hubert Lin, Chia-Che Chang, Yu-Sheng Chen, Da-Cheng Juan, Wei Wei, Hwann-Tzong Chen

The fact that the patch generation process is independent to each other inspires a wide range of new applications: firstly, "Patch-Inspired Image Generation" enables us to generate the entire image based on a single patch.

Image Generation Scene Generation

COCO-GAN: Generation by Parts via Conditional Coordinating

1 code implementation ICCV 2019 Chieh Hubert Lin, Chia-Che Chang, Yu-Sheng Chen, Da-Cheng Juan, Wei Wei, Hwann-Tzong Chen

On the computation side, COCO-GAN has a built-in divide-and-conquer paradigm that reduces memory requisition during training and inference, provides high-parallelism, and can generate parts of images on-demand.

Face Generation

Knowledge Distillation with Feature Maps for Image Classification

no code implementations3 Dec 2018 Wei-Chun Chen, Chia-Che Chang, Chien-Yu Lu, Che-Rung Lee

One promising method is knowledge distillation (KD), which creates a fast-to-execute student model to mimic a large teacher network.

Classification General Classification +2

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