  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.
