Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph

Dacheng Yin, Xuanchi Ren, Chong Luo, Yuwang Wang, Zhiwei Xiong, Wenjun Zeng

Keywords: Content-style decomposed representation, Zero-shot voice conversion, Style transfer, Transformer, Unsupervised learning

Abstract: This paper addresses the unsupervised learning of content-style decomposed representation. We first give a definition of style and then model the content-style representation as a token-level bipartite graph. An unsupervised framework, named Retriever, is proposed to learn such representations. First, a cross-attention module is employed to retrieve permutation invariant (P.I.) information, defined as style, from the input data. Second, a vector quantization (VQ) module is used, together with man-induced constraints, to produce interpretable content tokens. Last, an innovative link attention module serves as the decoder to reconstruct data from the decomposed content and style, with the help of the linking keys. Being modal-agnostic, the proposed Retriever is evaluated in both speech and image domains. The state-of-the-art zero-shot voice conversion performance confirms the disentangling ability of our framework. Top performance is also achieved in the part discovery task for images, verifying the interpretability of our representation. In addition, the vivid part-based style transfer quality demonstrates the potential of Retriever to support various fascinating generative tasks. Project page at https://ydcustc.github.io/retriever-demo/.

One-sentence Summary: We propose a model-agnostic and unsupervised framework to learn a novel token-level bipartite graph representation of content and style from structured input.

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:2202.12307/code)