Domain adaptation is proposed to deal with the challenging problem where the probability distribution of the training source is different from the testing target. Recently, adversarial learning has become the dominating technique for domain adaptation. Usually, adversarial domain adaptation methods simultaneously train a feature learner and a domain discriminator to learn domain-invariant features. Accordingly, how to effectively train the domain-adversarial model to learn domain-invariant features becomes a challenge in the community. To this end, we propose in this article a novel domain adaptation scheme named adversarial entropy optimization (AEO) to address the challenge. Specifically, we minimize the entropy when samples are from the independent distributions of source domain or target domain to improve the discriminability of the model. At the same time, we maximize the entropy when features are from the combined distribution of source domain and target domain so that the domain discriminator can be confused and the transferability of representations can be promoted. This minimax regime is well matched with the core idea of adversarial learning, empowering our model with transferability as well as discriminability for domain adaptation tasks. Also, AEO is flexible and compatible with different deep networks and domain adaptation frameworks. Experiments on five data sets show that our method can achieve state-of-the-art performance across diverse domain adaptation tasks.

中文翻译：

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无监督域适应的对抗性熵优化


域自适应被提出来处理训练源的概率分布与测试目标不同的挑战性问题。最近，对抗性学习已成为领域适应的主导技术。通常，对抗性域适应方法同时训练特征学习器和域鉴别器来学习域不变特征。因此，如何有效地训练领域对抗模型来学习领域不变特征成为社区面临的挑战。为此，我们在本文中提出了一种名为对抗性熵优化（AEO）的新型域适应方案来应对这一挑战。具体来说，当样本来自源域或目标域的独立分布时，我们最小化熵，以提高模型的可区分性。同时，当特征来自源域和目标域的组合分布时，我们最大化熵，以便可以混淆域鉴别器并提高表示的可迁移性。这种极小极大机制与对抗性学习的核心思想非常匹配，使我们的模型具有可迁移性以及领域适应任务的可区分性。此外，AEO 非常灵活，并且兼容不同的深度网络和域适应框架。对五个数据集的实验表明，我们的方法可以在不同的领域适应任务中实现最先进的性能。