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.

本文提出了一种新的自解耦框架 (SAD) 以实现特定领域的适应。使用一个领域关键字创建器和自对抗正则化器，将潜在表示分解为特定领域和不变领域特征，从而减缓了领域内差距，并取得了在目标检测和语义分割等领域中超越现有最先进方法的一致改进。

特定领域自对抗解缠

Self-Adversarial Disentangling for Specific Domain Adaptation

Convolutional neural network-based approaches have achieved remarkable
progress in semantic segmentation. However, these approaches heavily rely on
annotated data which are labor intensive. To cope with this limitation,
automatically annotated data generated from graphic engines are used to train
segmentation models. However, the models trained from synthetic data are
difficult to transfer to real images. To tackle this issue, previous works have
considered directly adapting models from the source data to the unlabeled
target data (to reduce the inter-domain gap). Nonetheless, these techniques do
not consider the large distribution gap among the target data itself
(intra-domain gap). In this work, we propose a two-step self-supervised domain
adaptation approach to minimize the inter-domain and intra-domain gap together.
First, we conduct the inter-domain adaptation of the model; from this
adaptation, we separate the target domain into an easy and hard split using an
entropy-based ranking function. Finally, to decrease the intra-domain gap, we
propose to employ a self-supervised adaptation technique from the easy to the
hard split. Experimental results on numerous benchmark datasets highlight the
effectiveness of our method against existing state-of-the-art approaches. The
source code is available at this https URL

本文提出了一个两步自监督域自适应方法，以最小化域间和域内差距，首先进行模型的域间自适应，然后使用基于熵的排序函数将目标域分为易和难两部分，最终采用自监督自适应技术从易到难来降低域内差距。