Domain generalization models aim to learn cross-domain knowledge from source
domain data, to improve performance on unknown target domains. Recent research
has demonstrated that diverse and rich source domain samples can enhance domain
generalization capability. This paper argues that the impact of each sample on
the model's generalization ability varies. Despite its small scale, a
high-quality dataset can still attain a certain level of generalization
ability. Motivated by this, we propose a domain-adversarial active learning
(DAAL) algorithm for classification tasks in domain generalization. First, we
analyze that the objective of tasks is to maximize the inter-class distance
within the same domain and minimize the intra-class distance across different
domains. To achieve this objective, we design a domain adversarial selection
method that prioritizes challenging samples. Second, we posit that even in a
converged model, there are subsets of features that lack discriminatory power
within each domain. We attempt to identify these feature subsets and optimize
them by a constraint loss. We validate and analyze our DAAL algorithm on
multiple domain generalization datasets, comparing it with various domain
generalization algorithms and active learning algorithms. Our results
demonstrate that the DAAL algorithm can achieve strong generalization ability
with fewer data resources, thereby reducing data annotation costs in domain
generalization tasks.

该研究提出了一种领域对抗主动学习算法（DAAL），用于领域泛化任务中的分类任务，在减少数据资源的情况下实现强大的泛化能力，从而降低领域泛化任务中的数据标注成本。

域对抗主动学习用于领域泛化分类

Domain Adversarial Active Learning for Domain Generalization  Classification

Obtaining accurate and valid information for drug molecules is a crucial and
challenging task. However, chemical knowledge and information have been
accumulated over the past 100 years from various regions, laboratories, and
experimental purposes. Little has been explored in terms of the
out-of-distribution (OOD) problem with noise and inconsistency, which may lead
to weak robustness and unsatisfied performance. This study proposes a novel
benchmark ADMEOOD, a systematic OOD dataset curator and benchmark specifically
designed for drug property prediction. ADMEOOD obtained 27 ADME (Absorption,
Distribution, Metabolism, Excretion) drug properties from Chembl and relevant
literature. Additionally, it includes two kinds of OOD data shifts: Noise Shift
and Concept Conflict Drift (CCD). Noise Shift responds to the noise level by
categorizing the environment into different confidence levels. On the other
hand, CCD describes the data which has inconsistent label among the original
data. Finally, it tested on a variety of domain generalization models, and the
experimental results demonstrate the effectiveness of the proposed partition
method in ADMEOOD: ADMEOOD demonstrates a significant difference performance
between in-distribution and out-of-distribution data. Moreover, ERM (Empirical
Risk Minimization) and other models exhibit distinct trends in performance
across different domains and measurement types.

该研究提出了一种针对药物属性预测的系统的 OOD 数据集整理器和基准 ADMEOOD，其中包括噪声转移和概念冲突漂移两种 OOD 数据转移方式，并经过多个领域泛化模型测试，实验结果表明 ADMEOOD 在分布内和分布外数据上有明显不同的性能表现，ERM 和其他模型在不同领域和测量类型上展现出不同的趋势。