Unpaired Image-to-Image Translation (UIT) focuses on translating images among
different domains by using unpaired data, which has received increasing
research focus due to its practical usage. However, existing UIT schemes defect
in the need of supervised training, as well as the lack of encoding domain
information. In this paper, we propose an Attribute Guided UIT model termed
AGUIT to tackle these two challenges. AGUIT considers multi-modal and
multi-domain tasks of UIT jointly with a novel semi-supervised setting, which
also merits in representation disentanglement and fine control of outputs.
Especially, AGUIT benefits from two-fold: (1) It adopts a novel semi-supervised
learning process by translating attributes of labeled data to unlabeled data,
and then reconstructing the unlabeled data by a cycle consistency operation.
(2) It decomposes image representation into domain-invariant content code and
domain-specific style code. The redesigned style code embeds image style into
two variables drawn from standard Gaussian distribution and the distribution of
domain label, which facilitates the fine control of translation due to the
continuity of both variables. Finally, we introduce a new challenge, i.e.,
disentangled transfer, for UIT models, which adopts the disentangled
representation to translate data less related with the training set. Extensive
experiments demonstrate the capacity of AGUIT over existing state-of-the-art
models.

本研究针对虽然已受到广泛研究但是存在监督训练需求及缺乏编码域信息等问题的不配对图像转换（UIT）提出了一种称为 AGUIT 的属性引导 UIT 模型。AGUIT 通过使用新颖的半监督设置，共同考虑 UIT 的多模态和多域任务，在表示去耦和输出细控制方面具有优点。该研究通过广泛实验表明了 AGUIT 相比现有最先进模型的优越性，同时提出了 UIT 模型的一个新挑战，即采用去耦合表示来翻译与训练集不相关的数据。

半监督学习下的属性指导非配对图像翻译

Attribute Guided Unpaired Image-to-Image Translation with  Semi-supervised Learning

Few-shot Learning aims to learn classifiers for new classes with only a few
training examples per class. Existing meta-learning or metric-learning based
few-shot learning approaches are limited in handling diverse domains with
various number of labels. The meta-learning approaches train a meta learner to
predict weights of homogeneous-structured task-specific networks, requiring a
uniform number of classes across tasks. The metric-learning approaches learn
one task-invariant metric for all the tasks, and they fail if the tasks
diverge. We propose to deal with these limitations with meta metric learning.
Our meta metric learning approach consists of task-specific learners, that
exploit metric learning to handle flexible labels, and a meta learner, that
discovers good parameters and gradient decent to specify the metrics in
task-specific learners. Thus the proposed model is able to handle unbalanced
classes as well as to generate task-specific metrics. We test our approach in
the `$k$-shot $N$-way' few-shot learning setting used in previous work and new
realistic few-shot setting with diverse multi-domain tasks and flexible label
numbers. Experiments show that our approach attains superior performances in
both settings.

本文提出一种基于元度量学习的学习方法，在 $k$-shot $N$-way 和多样化领域任务及灵活标签数量等新设置中均具有优异性能。