The domain adversarial neural network (DANN) methods have been successfully proposed and attracted much attention recently. In DANNs, a discriminator is trained to discriminate the domain labels of features generated by a generator, whereas the generator attempts to confuse it such that the distributions between domains are aligned. As a result, it actually encourages the whole alignment or transfer between domains, while the inter-class discriminative information across domains is not considered. In this paper, we present a Discrimination-Aware Domain Adversarial Neural Network (DA2NN) method to introduce the discriminative information or the discrepancy of inter-class instances across domains into deep domain adaptation. DA2NN considers both the alignment within the same class and the separation among different classes across domains in knowledge transfer via multiple discriminators. Empirical results show that DA2NN can achieve better classification performance compared with the DANN methods.

中文翻译：

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歧视感知域对抗神经网络

领域对抗性神经网络（DANN）方法最近被成功提出并引起了广泛关注。在 DANN 中，鉴别器被训练来区分由生成器生成的特征的域标签，而生成器试图混淆它以使域之间的分布对齐。结果，它实际上鼓励了域之间的整体对齐或转移，而没有考虑跨域的类间判别信息。在本文中，我们提出了一种区分感知域对抗神经网络（DA2NN）方法，将跨域的区分信息或类间实例的差异引入到深度域适应中。DA2NN 通过多个鉴别器在知识转移中考虑同一类内的对齐和跨域的不同类之间的分离。实证结果表明，与 DANN 方法相比，DA2NN 可以实现更好的分类性能。