Yu-Ying Yeh

Hi. I'm Yu-Ying Yeh.


About Me

I am a research assistant at National Taiwan University. My research interest mainly focuses on machine learning and computer vision. I studied Physics and Economics during my undergraduate study and now I fall in love with computer science and focus on research in computer science.


National Tsing Hua University

Non-degree, Computer Science

National Chiao Tung University

Non-degree, Computer Science

National Taiwan University

B.S. in Physics & B.A. in Economics

Work Experience

Research Assistant, National Taiwan University

Vision and Learning Lab

Supervised by Prof. Yu-Chiang Frank Wang

Research Assistant, Academia Sinica

Multimedia and Maching Learning Lab

Supervised by Dr. Yu-Chiang Frank Wang

Assistant Structured Product Manager, Cathay United Bank


Here are my recent research projects.

Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation

Yen-Cheng Liu, Yu-Ying Yeh, Tzu-Chien Fu, Wei-Chen Chiu, Sheng-De Wang, Yu-Chiang Frank Wang (Published in CVPR 2018)

Full paper: [Arxiv] / Code: To be updated soon.

While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated. However, one cannot easily address this task without observing ground truth annotation for the training data. To address this problem, we propose a novel deep learning model of Cross-Domain Representation Disentangler (CDRD). By observing fully annotated source-domain data and unlabeled target-domain data of interest, our model bridges the information across data domains and transfers the attribute information accordingly. Thus, cross-domain joint feature disentanglement and adaptation can be jointly performed. In the experiments, we provide qualitative results to verify our disentanglement capability. Moreover, we further confirm that our model can be applied for solving classification tasks of unsupervised domain adaptation, and performs favorably against state-of-the-art image disentanglement and translation methods.


Generative model

Tensorflow implementation of Variational Autoencoder and Generative Adversarial Networks.