Domain Adversarial Active Learning for Domain Generalization
  Classification
Jianting Chen Ling Ding Yunxiao Yang Zaiyuan Di Yang Xiang
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.