Multimodal supervised image translation. Issue 4 (1st February 2019)

• Record Type: Journal Article
• Authors: Ruan, Congcong; Chen, Dihu; Hu, Haifeng
• Is Part Of: Electronics letters. Volume 55:Issue 4(2019)
• Page Start: 190
• Page End: 192
• Publication Date: 2019-02-01
• Subjects: learning (artificial intelligence) -- expectation‐maximisation algorithm -- Gaussian distribution; images translation -- multimodal image‐to‐image translation -- graphics problems -- source domain -- target domain -- diverse results -- important problem -- special probabilistic nature -- Gaussian distribution -- conventional Gaussian vectors -- generated images -- linear distribution; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2018.6167
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : Multimodal image‐to‐image translation is a class of vision and graphics problems where the goal is to learn a one‐to‐many mapping between the source domain and target domain. Given an image in the source domain, the model aims to produce as many diverse results as possible. It is an important and challenging problem in the task of image translation. To this end, recent works utilise Gaussian vectors to produce diverse results but with a small difference. It is because of the special probabilistic nature of Gaussian distribution. In this work, the authors propose linearly distributed latent codes instead of conventional Gaussian vectors, which control the style of generated images. Taking advantage of linear distribution, their model can produce much more diverse results and outperform the state‐of‐the‐art baselines in terms of diversity. Qualitative and quantitative comparisons against baselines demonstrate the effectiveness and superiority of their method.