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

With social media being a major force in information consumption, accelerated
propagation of fake news has presented new challenges for platforms to
distinguish between legitimate and fake news. Effective fake news detection is
a non-trivial task due to the diverse nature of news domains and expensive
annotation costs. In this work, we address the limitations of existing
automated fake news detection models by incorporating auxiliary information
(e.g., user comments and user-news interactions) into a novel reinforcement
learning-based model called \textbf{RE}inforced \textbf{A}daptive
\textbf{L}earning \textbf{F}ake \textbf{N}ews \textbf{D}etection (REAL-FND).
REAL-FND exploits cross-domain and within-domain knowledge that makes it robust
in a target domain, despite being trained in a different source domain.
Extensive experiments on real-world datasets illustrate the effectiveness of
the proposed model, especially when limited labeled data is available in the
target domain.

该论文探讨了社交媒体中假新闻传播的挑战，并引入基于增强学习的模型（REAL-FND），将用户评论和用户 - 新闻交互等辅助信息结合起来，利用跨领域以及领域内的知识，特别是在目标领域的数据有限的情况下，有效地检测假新闻。

基于强化学习的领域自适应假新闻检测

Domain Adaptive Fake News Detection via Reinforcement Learning

With the rapid evolution of social media, fake news has become a significant
social problem, which cannot be addressed in a timely manner using manual
investigation. This has motivated numerous studies on automating fake news
detection. Most studies explore supervised training models with different
modalities (e.g., text, images, and propagation networks) of news records to
identify fake news. However, the performance of such techniques generally drops
if news records are coming from different domains (e.g., politics,
entertainment), especially for domains that are unseen or rarely-seen during
training. As motivation, we empirically show that news records from different
domains have significantly different word usage and propagation patterns.
Furthermore, due to the sheer volume of unlabelled news records, it is
challenging to select news records for manual labelling so that the
domain-coverage of the labelled dataset is maximized. Hence, this work: (1)
proposes a novel framework that jointly preserves domain-specific and
cross-domain knowledge in news records to detect fake news from different
domains; and (2) introduces an unsupervised technique to select a set of
unlabelled informative news records for manual labelling, which can be
ultimately used to train a fake news detection model that performs well for
many domains while minimizing the labelling cost. Our experiments show that the
integration of the proposed fake news model and the selective annotation
approach achieves state-of-the-art performance for cross-domain news datasets,
while yielding notable improvements for rarely-appearing domains in news
datasets.

本研究介绍了一种新的自动检测跨领域新闻中虚假消息的技术，该技术整合了领域特定和跨领域知识，并利用一种无监督的选择性注释技术来减少标注成本，并在跨领域的新闻数据集上实现了最先进的性能。