Domain adaptation aims to bridge the domain shifts between the source and the target domain. These shifts may span different dimensions such as fog, rainfall, etc. However, recent methods typically do not consider explicit prior knowledge about the domain shifts on a specific dimension, thus leading to less desired adaptation performance. In this paper, we study a practical setting called Specific Domain Adaptation (SDA) that aligns the source and target domains in a demanded-specific dimension. Within this setting, we observe the intra-domain gap induced by different domainness (i.e., numerical magnitudes of domain shifts in this dimension) is crucial when adapting to a specific domain. To address the problem, we propose a novel Self-Adversarial Disentangling (SAD) framework. In particular, given a specific dimension, we first enrich the source domain by introducing a domainness creator with providing additional supervisory signals. Guided by the created domainness, we design a self-adversarial regularizer and two loss functions to jointly disentangle the latent representations into domainness-specific and domainness-invariant features, thus mitigating the intra-domain gap. Our method can be easily taken as a plug-and-play framework and does not introduce any extra costs in the inference time. We achieve consistent improvements over state-of-the-art methods in both object detection and semantic segmentation.
翻译:域适应旨在弥合源和目标领域之间的域变。这些变迁可能涉及不同层面,如雾、降雨等。然而,最近的方法通常不考虑对特定层面域变现的明确先前知识,从而导致不那么理想的适应性表现。在本文件中,我们研究了一个叫作具体域变迁(SDA)的实用环境,该环境将源和目标领域与特定层面相匹配。在此环境中,我们观察到不同域化(即这一层面域变迁的数值大小)引发的域内差距,在适应特定领域时至关重要。然而,为了解决这个问题,我们提出一个新的“自我反射”框架(SAD),特别是考虑到一个特定层面,我们首先通过提供额外的监管信号,来丰富源域域域化创建者。在设定域性时,我们设计了一种自我对抗调节器和两个损失功能,以联合消除域内和域内变换特性的隐性表现,从而缩小内部的距离。我们的方法可以很容易地在固定的域变换成本和结构中实现。我们的方法可以很容易地在固定式的框架中实现。