In this paper, we solve the problem of adapting classifiers across domains.
We consider the problem of domain adaptation for multi-class classification
where we are provided a labeled set of examples in a source dataset and we are
provided a target dataset with no supervision. In this setting, we propose an
adversarial discriminator based approach. While the approach based on
adversarial discriminator has been previously proposed; in this paper, we
present an informed adversarial discriminator. Our observation relies on the
analysis that shows that if the discriminator has access to all the information
available including the class structure present in the source dataset, then it
can guide the transformation of features of the target set of classes to a more
structure adapted space. Using this formulation, we obtain state-of-the-art
results for the standard evaluation on benchmark datasets. We further provide
detailed analysis which shows that using all the labeled information results in
an improved domain adaptation.

针对多类分类问题的领域适应问题，我们提出了一种基于对抗鉴别器的方法，并采用了有关分类结构的信息，得到了在基准数据集上的最优结果。

回顾标签：一种基于类别的领域自适应技术

Looking back at Labels: A Class based Domain Adaptation Technique

We address the computational problem of novel human pose synthesis. Given an
image of a person and a desired pose, we produce a depiction of that person in
that pose, retaining the appearance of both the person and background. We
present a modular generative neural network that synthesizes unseen poses using
training pairs of images and poses taken from human action videos. Our network
separates a scene into different body part and background layers, moves body
parts to new locations and refines their appearances, and composites the new
foreground with a hole-filled background. These subtasks, implemented with
separate modules, are trained jointly using only a single target image as a
supervised label. We use an adversarial discriminator to force our network to
synthesize realistic details conditioned on pose. We demonstrate image
synthesis results on three action classes: golf, yoga/workouts and tennis, and
show that our method produces accurate results within action classes as well as
across action classes. Given a sequence of desired poses, we also produce
coherent videos of actions.

本文介绍了一种基于模块化生成神经网络的方法，用来合成出一张人的图像并保证姿势、外貌和背景的一致性，其中包括了基于图像和动作的训练资料，以及对抗性判别器等技术来实现姿势合成，最终能够生成与动作类别准确匹配的图像，同时还可以将多个动作合成为视频。