Zero-Pair Image to Image Translation using Domain Conditional Normalization

• URL: http://arxiv.org/abs/2011.05680v1
• Date: Wed, 11 Nov 2020 10:20:47 GMT
• Title: Zero-Pair Image to Image Translation using Domain Conditional Normalization
• Authors: Samarth Shukla, Andr\'es Romero, Luc Van Gool, Radu Timofte
• Abstract summary: We propose an approach based on domain conditional normalization (DCN) for zero-pair image-to-image translation. We employ a single generator which has an encoder-decoder structure and analyze different implementations of domain conditional normalization to obtain the desired target domain output.
• Score: 138.7878582237908
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose an approach based on domain conditional normalization (DCN) for zero-pair image-to-image translation, i.e., translating between two domains which have no paired training data available but each have paired training data with a third domain. We employ a single generator which has an encoder-decoder structure and analyze different implementations of domain conditional normalization to obtain the desired target domain output. The validation benchmark uses RGB-depth pairs and RGB-semantic pairs for training and compares performance for the depth-semantic translation task. The proposed approaches improve in qualitative and quantitative terms over the compared methods, while using much fewer parameters. Code available at https://github.com/samarthshukla/dcn

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