Abstract Adversarial networks have been used to learn transferable representations in many domain adaptation methods. However, there is no theoretical guarantee that two distributions are identical, even if the discriminator is fully confused. Therefore, a more elaborate domain adversarial method to better align distributions is desirable. In this paper, we propose two groups of multiple adversarial networks for domain-invariant and class-distinguishable feature learning: i) class-wise domain adversarial networks based on sample locations and ii) sample adversarial networks. We analyze the impact of the sample’s intra-class distribution on transfer learning and reveal that the distance between a sample and its cluster center affects the role of that sample in transfer learning. Domain adversarial learning is conducted separately on samples located near the cluster center (central samples) and samples located far away from cluster center (non-central samples) in order to align the distributions of the source domain and target domain better. However, separate domain adversarial learning on central and non-central samples ignores the relationship between them, because all these samples belong to the same class. We therefore propose a method called sample adversarial learning to convert the distribution of non-central samples to the distribution of central samples. In this way, the relationship between central samples and non-central samples is rebuilt. Sample adversarial learning solves the problem that arises in the sperate domain adversarial learning. Sample adversarial learning also enables us to obtain a class-distinguishable feature representation because of the reduction in intra-class distance. Experimental results show that our method extracts more transferable and class-distinguishable features than existing methods and achieves start-of-the-art results on several datasets. The significant contribution of this paper is to show how separate domain adversarial learning based on sample locations and sample adversarial learning together enhance positive transfer by maximally matching the multimodal structures underlying the data distributions across domains.

中文翻译：

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基于多对抗网络的域不变和类可区分特征学习的域适应

摘要 对抗网络已被用于在许多领域适应方法中学习可迁移表示。但是，即使判别器完全混淆，也不能保证两个分布完全相同。因此，需要一种更精细的域对抗方法来更好地对齐分布。在本文中，我们提出了两组用于域不变和类可区分特征学习的多对抗网络：i）基于样本位置的类域对抗网络和 ii）样本对抗网络。我们分析了样本的类内分布对迁移学习的影响，并揭示了样本与其聚类中心之间的距离会影响该样本在迁移学习中的作用。为了更好地对齐源域和目标域的分布，分别对靠近聚类中心的样本（中心样本）和远离聚类中心的样本（非中心样本）进行领域对抗学习。然而，对中心样本和非中心样本进行单独域对抗学习忽略了它们之间的关系，因为所有这些样本都属于同一类。因此，我们提出了一种称为样本对抗学习的方法，将非中心样本的分布转换为中心样本的分布。这样，中心样本和非中心样本之间的关系就被重建了。样本对抗学习解决了单独域对抗学习中出现的问题。由于类内距离的减少，样本对抗学习还使我们能够获得类可区分的特征表示。实验结果表明，我们的方法比现有方法提取了更多可转移和可区分的特征，并在多个数据集上取得了一流的结果。本文的重要贡献是展示了基于样本位置的单独域对抗学习和样本对抗学习如何通过最大限度地匹配跨域数据分布的多模态结构来共同增强正迁移。实验结果表明，我们的方法比现有方法提取了更多可转移和可区分的特征，并在多个数据集上取得了一流的结果。本文的重要贡献是展示了基于样本位置的单独域对抗学习和样本对抗学习如何通过最大限度地匹配跨域数据分布的多模态结构来共同增强正迁移。实验结果表明，我们的方法比现有方法提取了更多可转移和可区分的特征，并在多个数据集上取得了一流的结果。本文的重要贡献是展示了基于样本位置的单独域对抗学习和样本对抗学习如何通过最大限度地匹配跨域数据分布的多模态结构来共同增强正迁移。