Unsupervised domain translation (UDT) aims to find functions that convert
samples from one domain (e.g., sketches) to another domain (e.g., photos)
without changing the high-level semantic meaning (also referred to as
``content''). The translation functions are often sought by probability
distribution matching of the transformed source domain and target domain.
CycleGAN stands as arguably the most representative approach among this line of
work. However, it was noticed in the literature that CycleGAN and variants
could fail to identify the desired translation functions and produce
content-misaligned translations. This limitation arises due to the presence of
multiple translation functions -- referred to as ``measure-preserving
automorphism" (MPA) -- in the solution space of the learning criteria. Despite
awareness of such identifiability issues, solutions have remained elusive. This
study delves into the core identifiability inquiry and introduces an MPA
elimination theory. Our analysis shows that MPA is unlikely to exist, if
multiple pairs of diverse cross-domain conditional distributions are matched by
the learning function. Our theory leads to a UDT learner using distribution
matching over auxiliary variable-induced subsets of the domains -- other than
over the entire data domains as in the classical approaches. The proposed
framework is the first to rigorously establish translation identifiability
under reasonable UDT settings, to our best knowledge. Experiments corroborate
with our theoretical claims.

无监督领域转换 (UDT) 通过概率分布匹配转化一个领域的样本到另一个领域，以找到保持高层语义意义的转换函数。然而，潜在的问题是存在多个转换函数，导致无法准确识别并产生错误的转换结果。本研究对这一识别问题进行了深入探讨，并提出了一种消除多个转换函数的理论。通过实验证明，在合理的 UDT 设置下，使用基于分布匹配的辅助变量诱导子集的方法可以更好地确立好的翻译识别。

基于多样化分布匹配的可识别无监督领域转换

Towards Identifiable Unsupervised Domain Translation: A Diversified  Distribution Matching Approach

While developing new unsupervised domain translation methods for endoscopy
videos, it is typical to start with approaches that initially work for
individual frames without temporal consistency. Once an individual-frame model
has been finalized, additional contiguous frames are added with a modified deep
learning architecture to train a new model for temporal consistency. This
transition to temporally-consistent deep learning models, however, requires
significantly more computational and memory resources for training. In this
paper, we present a lightweight solution with a tunable temporal parameter,
RT-GAN (Recurrent Temporal GAN), for adding temporal consistency to individual
frame-based approaches that reduces training requirements by a factor of 5. We
demonstrate the effectiveness of our approach on two challenging use cases in
colonoscopy: haustral fold segmentation (indicative of missed surface) and
realistic colonoscopy simulator video generation. The datasets, accompanying
code, and pretrained models will be made available at
https://github.com/nadeemlab/CEP.

通过 RT-GAN（循环时间 GAN）的轻量级可调的时间参数，为单帧方法添加时间一致性，将培训需求降低了 5 倍，从而在内窥镜视频的开发中展示了其有效性，包括肠镜检查中忽略表面的构壁褶皱分割和逼真的肠镜模拟器视频生成。

RT-GAN：用于在基于帧的领域转换方法中增加轻量级时间一致性的循环时序生成对抗网络

RT-GAN: Recurrent Temporal GAN for Adding Lightweight Temporal  Consistency to Frame-Based Domain Translation Approaches

While unsupervised domain translation (UDT) has seen a lot of success
recently, we argue that mediating its translation via categorical semantic
features could broaden its applicability. In particular, we demonstrate that
categorical semantics improves the translation between perceptually different
domains sharing multiple object categories. We propose a method to learn, in an
unsupervised manner, categorical semantic features (such as object labels) that
are invariant of the source and target domains. We show that conditioning the
style encoder of unsupervised domain translation methods on the learned
categorical semantics leads to a translation preserving the digits on
MNIST$\leftrightarrow$SVHN and to a more realistic stylization on
Sketches$\to$Reals.

本文提出，利用分类语义特征对无监督领域翻译进行干预可以拓宽其适用性，通过学习语义特征（如对象标签）的方法，可以更好地保持数字的传输和能够实现更逼真的描绘。