Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph
Dacheng Yin*, Xuanchi Ren*, Chong Luo, Yuwang Wang, Zhiwei Xiong and Wenjun Zeng
ICLR 2022
๐ฒ Merge and Clean Code
We are cleaning and merging the code and hope to release it very soon.
For the vision part, we provide a sample (uncleaned) code here.
For the audio part, we provide a sample (uncleaned) code here.
In this repo, we propose an unsupervised and modality-agnostic content-style disentanglement framework: Retriever. We demonstrate that our learned representation can benefit zero-shot voice conversion, co-part segmentation, and style transfer.
@inproceedings{yin2022Retriever,
title = {Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph},
author = {Yin, Dacheng and Ren, Xuanchi and Luo, Chong and Wang, Yuwang, and Xiong, Zhiwei, and Zeng, Wenjun},
booktitle = {ICLR},
year = {2022}
}