10/5/2023 0 Comments Photo translateGuided image-to-image translation with bi-directional feature transformation. For quantitative evaluations, we measure realism with user study and Fréchet inception distance, and measure diversity with the perceptual distance metric, Jensen–Shannon divergence, and number of statistically-different bins.ĪlBahar, B., & Huang, J. Qualitative results show that our model can generate diverse and realistic images on a wide range of tasks without paired training data. To handle unpaired training data, we introduce a cross-cycle consistency loss based on disentangled representations. Our model takes the encoded content features extracted from a given input and attribute vectors sampled from the attribute space to synthesize diverse outputs at test time. To synthesize diverse outputs, we propose to embed images onto two spaces: a domain-invariant content space capturing shared information across domains and a domain-specific attribute space. In this work, we present an approach based on disentangled representation for generating diverse outputs without paired training images. There are two main challenges for this task: (1) lack of aligned training pairs and (2) multiple possible outputs from a single input image. Image-to-image translation aims to learn the mapping between two visual domains.
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