Unsupervised domain adaptation (UDA) methods for learning domain invariant
representations have achieved remarkable progress. However, most of the studies
were based on direct adaptation from the source domain to the target domain and
have suffered from large domain discrepancies. In this paper, we propose a UDA
method that effectively handles such large domain discrepancies. We introduce a
fixed ratio-based mixup to augment multiple intermediate domains between the
source and target domain. From the augmented-domains, we train the
source-dominant model and the target-dominant model that have complementary
characteristics. Using our confidence-based learning methodologies, e.g.,
bidirectional matching with high-confidence predictions and self-penalization
using low-confidence predictions, the models can learn from each other or from
its own results. Through our proposed methods, the models gradually transfer
domain knowledge from the source to the target domain. Extensive experiments
demonstrate the superiority of our proposed method on three public benchmarks:
Office-31, Office-Home, and VisDA-2017.

介绍了一种针对领域差异性大的无监督域自适应方法，包括使用固定比例的混合增强多个中间域，训练源主导模型和目标主导模型，使用置信度学习方法逐渐实现领域知识的转移。

FixBi: 跨领域空间的无监督领域自适应

FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) has attracted a lot of attention in the
last ten years. The emergence of Domain Invariant Representations (IR) has
improved drastically the transferability of representations from a labelled
source domain to a new and unlabelled target domain. However, a potential
pitfall of this approach, namely the presence of \textit{label shift}, has been
brought to light. Some works address this issue with a relaxed version of
domain invariance obtained by weighting samples, a strategy often referred to
as Importance Sampling. From our point of view, the theoretical aspects of how
Importance Sampling and Invariant Representations interact in UDA have not been
studied in depth. In the present work, we present a bound of the target risk
which incorporates both weights and invariant representations. Our theoretical
analysis highlights the role of inductive bias in aligning distributions across
domains. We illustrate it on standard benchmarks by proposing a new learning
procedure for UDA. We observed empirically that weak inductive bias makes
adaptation more robust. The elaboration of stronger inductive bias is a
promising direction for new UDA algorithms.

本文研究在无监督领域适应中，加权样本的重要性抽样策略与不变表征相互作用的理论方面，并提出了一种改进的学习算法，旨在加强归纳偏差，减轻适应过程中可能存在的标签偏移问题。