In recent years, machine learning has achieved impressive results across
different application areas. However, machine learning algorithms do not
necessarily perform well on a new domain with a different distribution than its
training set. Domain Adaptation (DA) is used to mitigate this problem. One
approach of existing DA algorithms is to find domain invariant features whose
distributions in the source domain are the same as their distribution in the
target domain. In this paper, we propose to let the classifier that performs
the final classification task on the target domain learn implicitly the
invariant features to perform classification. It is achieved via feeding the
classifier during training generated fake samples that are similar to samples
from both the source and target domains. We call these generated samples
domain-agnostic samples. To accomplish this we propose a novel variation of
generative adversarial networks (GAN), called the MiddleGAN, that generates
fake samples that are similar to samples from both the source and target
domains, using two discriminators and one generator. We extend the theory of
GAN to show that there exist optimal solutions for the parameters of the two
discriminators and one generator in MiddleGAN, and empirically show that the
samples generated by the MiddleGAN are similar to both samples from the source
domain and samples from the target domain. We conducted extensive evaluations
using 24 benchmarks; on the 24 benchmarks, we compare MiddleGAN against various
state-of-the-art algorithms and outperform the state-of-the-art by up to 20.1\%
on certain benchmarks.

本研究提出了一种新型生成对抗网络（MiddleGAN），通过输入源域和目标域相似的样本生成领域无关样本，以训练处理目标领域分类任务的分类器，通过在 24 个基准测试中的实验证明，在特定的基准中，MiddleGAN 在现有算法中的表现出了高达 20.1％的优异性能。