Composable Unpaired Image to Image Translation
There has been remarkable recent work in unpaired image-to-image translation. However, they're restricted to translation on single pairs of distributions, with some exceptions. In this study, we extend one of these works to a scalable multidistribution translation mechanism. Our translation models not only converts from one distribution to another but can be stacked to create composite translation functions. We show that this composite property makes it possible to generate images with characteristics not seen in the training set. We also propose a decoupled training mechanism to train multiple distributions separately, which we show, generates better samples than isolated joint training. Further, we do a qualitative and quantitative analysis to assess the plausibility of the samples. The code is made available at https://github.com/lgraesser/im2im2im.
Code (1)
Tasks
Image-to-Image TranslationTranslationSimilar Papers 제목 키워드 기반
Unpaired Image-to-Image Translation via Neural Schrödinger Bridge
Diffusion models are a powerful class of generative models which simulate stochastic differential equations (SDEs) to generate data from noise. While diffusion models have achieved remarkable progress, they have limitati…
Image-to-Image TranslationTranslationQuality-aware Unpaired Image-to-Image Translation
Generative Adversarial Networks (GANs) have been widely used for the image-to-image translation task. While these models rely heavily on the labeled image pairs, recently some GAN variants have been proposed to tackle th…
Image Quality AssessmentImage-to-Image TranslationTranslationITTR: Unpaired Image-to-Image Translation with Transformers
Unpaired image-to-image translation is to translate an image from a source domain to a target domain without paired training data. By utilizing CNN in extracting local semantics, various techniques have been developed to…
Image-to-Image TranslationTranslationLearning image-to-image translation using paired and unpaired training samples
Image-to-image translation is a general name for a task where an image from one domain is converted to a corresponding image in another domain, given sufficient training data. Traditionally different approaches have been…
Image-to-Image TranslationTranslationUnpaired Translation from Semantic Label Maps to Images by Leveraging Domain-Specific Simulations
Photorealistic image generation from simulated label maps are necessitated in several contexts, such as for medical training in virtual reality. With conventional deep learning methods, this task requires images that are…
Contrastive LearningImage GenerationTranslation