Progressive Energy-Based Cooperative Learning for Multi-Domain Image-to-Image Translation Article Swipe

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Weinan Song , Yaxuan Zhu , Lei He , Yingnian Wu , Jianwen Xie ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2306.14448

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.

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Generator (circuit theory) Computer science Image (mathematics) Domain (mathematical analysis) Encoder Artificial intelligence Translation (biology) Image translation Computer vision Algorithm Pattern recognition (psychology) Mathematics Power (physics) Quantum mechanics Biochemistry Gene Chemistry Operating system Messenger RNA Mathematical analysis Physics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2306.14448
PDF: https://arxiv.org/pdf/2306.14448
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4382322935

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DOI: https://doi.org/10.48550/arxiv.2306.14448

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Title: Progressive Energy-Based Cooperative Learning for Multi-Domain Image-to-Image Translation

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Language: en

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Publication year: 2023

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Publication date: 2023-06-26

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Authors: Weinan Song, Yaxuan Zhu, Lei He, Yingnian Wu, Jianwen Xie

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Landing page: https://arxiv.org/abs/2306.14448

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Concepts: Generator (circuit theory), Computer science, Image (mathematics), Domain (mathematical analysis), Encoder, Artificial intelligence, Translation (biology), Image translation, Computer vision, Algorithm, Pattern recognition (psychology), Mathematics, Power (physics), Quantum mechanics, Biochemistry, Gene, Chemistry, Operating system, Messenger RNA, Mathematical analysis, Physics

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institutions_distinct_count	5
sustainable_development_goals[0].id	https://metadata.un.org/sdg/7
sustainable_development_goals[0].score	0.7699999809265137
sustainable_development_goals[0].display_name	Affordable and clean energy
citation_normalized_percentile