An Active Multi-Target Domain Adaptation Strategy: Progressive Class Prototype Rectification

Abstract

Compared to single-source to single-target (1S1T) domain adaptation, single-source to multi-target (1SmT) domain adaptation is more practical but also more challenging. In 1SmT scenarios, the significant differences in feature distributions between various target domains increase the difficulty for models to adapt to multiple domains. Moreover, 1SmT requires effective transfer to each target domain while maintaining performance in the source domain, demanding higher generalization capabilities from the model. In 1S1T scenarios, active domain adaptation methods improve generalization by incorporating a few target domain samples, but these methods are rarely applied in 1SmT due to potential sampling bias and outlier interference. To address this, we propose Progressive Prototype Refinement (PPR), an active multi-target domain adaptation method combining 1SmT with active learning to enhance cross-domain knowledge transfer. Specifically, an uncertainty assessment strategy is used to select representative samples from multiple target domains, forming a candidate set for model training. Based on the Lindeberg--Levy central limit theorem, we sample from a Gaussian distribution using corrected prototype statistics to augment the classifier's feature input, allowing the model to learn transitional information between domains. Finally, a mapping matrix is used for cross-domain alignment, addressing incomplete class coverage and outlier interference. Extensive experiments on multiple benchmark datasets demonstrate PPR's superior performance, with a 6.35% improvement on the PACS dataset and a 17.32% improvement on the Remote Sensing dataset.