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.

翻译：近些年来,机器学习在不同应用领域取得了令人印象深刻的成果;然而,机器学习算法不一定在与培训范围不同的新领域取得良好效果,其分布方式与培训范围不同。 域适应(DA)用于缓解这一问题。 现有DA算法的一种方法是寻找在源域分布与目标领域分布相同的域别异特性。 在本文中,我们提议让执行目标领域最后分类任务的分类员隐含地学习进行分类的不易变特征。在培训产生的假样品中,通过向分类员提供与来源和目标领域样本相似的假样品来达到这一目的。 我们将这些样品称为样品的域别式样本。 为了实现这一目标,我们提议对基因对抗网络(GAN)进行新的变异,称为MiddleGAN, 生成的假样品与来源和目标领域的样品相似,使用两个歧视器和一个发电机。我们将GAN理论扩展至GAN理论,以表明存在两个歧视对象和一个发电机的参数的最佳解决办法。 我们从24个域域域标尺的样品到24个域标尺的样品,我们从24个域标尺的样品到24个地标尺的样品。