Training neural networks for source separation involves presenting a mixture recording at the input of the network and updating network parameters in order to produce an output that resembles the clean source. Consequently, supervised source separation depends on the availability of paired mixture-clean training examples. In this paper, we interpret source separation as a style transfer problem. We present a variational auto-encoder network that exploits the commonality across the domain of mixtures and the domain of clean sounds and learns a shared latent representation across the two domains. Using these cycle-consistent variational auto-encoders, we learn a mapping from the mixture domain to the domain of clean sounds and perform source separation without explicitly supervising with paired training examples.
More recently, GANs have also been successfully used for unsupervised image-to-image translation @cite_24 . To simplify the complexity of GAN based networks, Liu et.al., proposed the UNIT algorithm @cite_27 using Variational Auto-Encoders (VAEs) to learn generative models for the source and target domains. As described in @cite_27 , the source and target domain training examples define a marginal distribution for the source and target domains respectively. Further, the goal of UDT is to estimate the joint distribution of the two domains, given their marginal distributions. This problem is an ill-posed problem and requires additional assumptions on the joint distribution @cite_21 . To this end, Liu et. al., show that a necessary condition for UDT is to have a shared latent space between the source and target domains. This implies that a pair of points having the same content in the source and target domains should be mapped to the same point in the latent space. Since we do not have access to such paired training examples, we use additional consistency terms in the cost-function to enforce a shared latent space as described in .
@cite_24 @cite_27 @cite_21
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Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples. Our goal is to learn a mapping G : X → Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an adversarial loss. Because this mapping is highly under-constrained, we couple it with an inverse mapping F : Y → X and introduce a cycle consistency loss to push F(G(X)) ≈ X (and vice versa). Qualitative results are presented on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, photo enhancement, etc. Quantitative comparisons against several prior methods demonstrate the superiority of our approach. Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains. Since there exists an infinite set of joint distributions that can arrive the given marginal distributions, one could infer nothing about the joint distribution from the marginal distributions without additional assumptions. To address the problem, we make a shared-latent space assumption and propose an unsupervised image-to-image translation framework based on Coupled GANs. We compare the proposed framework with competing approaches and present high quality image translation results on various challenging unsupervised image translation tasks, including street scene image translation, animal image translation, and face image translation. We also apply the proposed framework to domain adaptation and achieve state-of-the-art performance on benchmark datasets. Code and additional results are available in https: github.com mingyuliutw unit. Preliminaries Discrete Theory Continuous Theory Inequalities Intensity-Governed Processes Diffusions Appendix Frequently Used Notation References Index.