Winter to summer Photograph to portrait Attribute Generated Input Output Input Output Fig. 1: Unpaired diverse image-to-image translation. (Lef t) Our model learns to perform diverse translation between two collections of images without aligned training pairs. (Right) Example-guided translation. Abstract. Image-to-image translation aims to learn the mapping between two visual domains. There are two main challenges for many applications: 1) the lack of aligned training pairs and 2) multiple possible outputs from a single input image. In this work, we present an approach based on disentangled representation for producing diverse outputs without paired training images. To achieve diversity, we propose to embed images onto two spaces: a domain-invariant content space capturing shared information across domains and a domain-specific attribute space. Our model takes the encoded content features extracted from a given input and the attribute vectors sampled from the attribute space to produce diverse outputs at test time. To handle unpaired training data, we introduce a novel cross-cycle consistency loss based on disentangled representations. Qualitative results show that our model can generate diverse and realistic images on a wide range of tasks without paired training data. For quantitative comparisons, we measure realism with user study and diversity with a perceptual distance metric. We apply the proposed model to domain adaptation and show competitive performance when compared to the state-of-the-art on the MNIST-M and the LineMod datasets. equal contribution
REF proposed a disentangled representation framework to generate diverse outputs with unpaired training data.
Diverse Image-to-Image Translation via Disentangled Representations
ArXiv
ArXiv
Computer Science