A typical domain adaptation approach is to adapt models trained on the
annotated data in a source domain (e.g., sunny weather) for achieving high
performance on the test data in a target domain (e.g., rainy weather). Whether
the target contains a single homogeneous domain or multiple heterogeneous
domains, existing works always assume that there exist clear distinctions
between the domains, which is often not true in practice (e.g., changes in
weather). We study an open compound domain adaptation (OCDA) problem, in which
the target is a compound of multiple homogeneous domains without domain labels,
reflecting realistic data collection from mixed and novel situations. We
propose a new approach based on two technical insights into OCDA: 1) a
curriculum domain adaptation strategy to bootstrap generalization across
domains in a data-driven self-organizing fashion and 2) a memory module to
increase the model's agility towards novel domains. Our experiments on digit
classification, facial expression recognition, semantic segmentation, and
reinforcement learning demonstrate the effectiveness of our approach.

本文提出了一种基于课程域适应策略和记忆模块的方法，以处理由多个同质域组成的无标签复合目标域的开放复合域适应问题。实验证明我们的方法在数字分类，面部表情识别，语义分割和强化学习上非常有效。

开放式复合领域自适应

Open Compound Domain Adaptation

We propose a new approach, called self-motivated pyramid curriculum domain
adaptation (PyCDA), to facilitate the adaptation of semantic segmentation
neural networks from synthetic source domains to real target domains. Our
approach draws on an insight connecting two existing works: curriculum domain
adaptation and self-training. Inspired by the former, PyCDA constructs a
pyramid curriculum which contains various properties about the target domain.
Those properties are mainly about the desired label distributions over the
target domain images, image regions, and pixels. By enforcing the segmentation
neural network to observe those properties, we can improve the network's
generalization capability to the target domain. Motivated by the self-training,
we infer this pyramid of properties by resorting to the semantic segmentation
network itself. Unlike prior work, we do not need to maintain any additional
models (e.g., logistic regression or discriminator networks) or to solve minmax
problems which are often difficult to optimize. We report state-of-the-art
results for the adaptation from both GTAV and SYNTHIA to Cityscapes, two
popular settings in unsupervised domain adaptation for semantic segmentation.

该研究提出了一种名为 PyCDA 的自我激励金字塔课程域自适应方法，用于将语义分割神经网络从合成源领域适应到真实目标领域中， 并在无监督域适应语义分割中得到了最先进的结果。