Unsupervised image-to-image translation is a class of computer vision
problems which aims at modeling conditional distribution of images in the
target domain, given a set of unpaired images in the source and target domains.
An image in the source domain might have multiple representations in the target
domain. Therefore, ambiguity in modeling of the conditional distribution
arises, specially when the images in the source and target domains come from
different modalities. Current approaches mostly rely on simplifying assumptions
to map both domains into a shared-latent space. Consequently, they are only
able to model the domain-invariant information between the two modalities.
These approaches usually fail to model domain-specific information which has no
representation in the target domain. In this work, we propose an unsupervised
image-to-image translation framework which maximizes a domain-specific
variational information bound and learns the target domain-invariant
representation of the two domain. The proposed framework makes it possible to
map a single source image into multiple images in the target domain, utilizing
several target domain-specific codes sampled randomly from the prior
distribution, or extracted from reference images.

本文介绍了一种无监督图像转换框架，该框架最大化了一个特定域的变分信息界，并学习了这两个域的目标域不变表示。

使用领域特定变分信息界限进行无监督图像到图像的翻译

Unsupervised Image-to-Image Translation Using Domain-Specific  Variational Information Bound

Unsupervised neural machine translation (NMT) is a recently proposed approach
for machine translation which aims to train the model without using any labeled
data. The models proposed for unsupervised NMT often use only one shared
encoder to map the pairs of sentences from different languages to a
shared-latent space, which is weak in keeping the unique and internal
characteristics of each language, such as the style, terminology, and sentence
structure. To address this issue, we introduce an extension by utilizing two
independent encoders but sharing some partial weights which are responsible for
extracting high-level representations of the input sentences. Besides, two
different generative adversarial networks (GANs), namely the local GAN and
global GAN, are proposed to enhance the cross-language translation. With this
new approach, we achieve significant improvements on English-German,
English-French and Chinese-to-English translation tasks.

通过使用两个独立的编码器和局部 GAN、全局 GAN，以提高跨语言翻译，本文通过无监督神经机器翻译方法显著提高了英德，英法和中英翻译任务的效果。