Progressive Energy-Based Cooperative Learning for Multi-Domain Image-to-Image Translation
CoRR(2023)
摘要
This paper studies a novel energy-based cooperative learning framework for
multi-domain image-to-image translation. The framework consists of four
components: descriptor, translator, style encoder, and style generator. The
descriptor is a multi-head energy-based model that represents a multi-domain
image distribution. The components of translator, style encoder, and style
generator constitute a diversified image generator. Specifically, given an
input image from a source domain, the translator turns it into a stylised
output image of the target domain according to a style code, which can be
inferred by the style encoder from a reference image or produced by the style
generator from a random noise. Since the style generator is represented as an
domain-specific distribution of style codes, the translator can provide a
one-to-many transformation (i.e., diversified generation) between source domain
and target domain. To train our framework, we propose a likelihood-based
multi-domain cooperative learning algorithm to jointly train the multi-domain
descriptor and the diversified image generator (including translator, style
encoder, and style generator modules) via multi-domain MCMC teaching, in which
the descriptor guides the diversified image generator to shift its probability
density toward the data distribution, while the diversified image generator
uses its randomly translated images to initialize the descriptor's Langevin
dynamics process for efficient sampling.
更多查看译文
关键词
cooperative learning,translation,energy-based,multi-domain,image-to-image
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要