Domain Adversarial Active Learning for Domain Generalization Classification

arXiv:2403.06174v1 Announce Type: new
Abstract: 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 …

abstract active learning adversarial aim arxiv capability classification cs.ai cs.lg data diverse domain domain knowledge domains impact knowledge learn paper performance research sample samples type