Rethinking Active Domain Adaptation: Balancing Uncertainty and Diversity

Abstract

In applications of machine learning, usually the test data domain distributes inconsistently with the model training data, implying they are not independent and identically distributed. To address this challenge with certain annotation knowledge, the paradigm of Active Domain Adaptation (ADA) has been proposed through selectively labeling some target instances to facilitate cross-domain alignment with minimal annotation cost. However, existing ADA methods often struggle to balance uncertainty and diversity in sample selection, limiting their effectiveness. To address this, we propose a novel ADA framework: Balancing Uncertainty and Diversity (ADA-BUD), which desirably achieves ADA while balancing the data uncertainty and diversity across domains. Specifically, in ADA-BUD, the Uncertainty Range Perception (URA) module is specially designed to distinguish these most informative but uncertain target instances for annotation while appraising not only each instance itself but also their neighbors. Subsequently, the module called Representative Energy Optimization (REO) is constructed to refine diversity of the resulting annotation instances set. Last but not least, to enhance the flexibility of ADA-BUD in handling scenarios with limited data, we further build the Dynamic Sample Enhancement (DSE) module in ADA-BUD to generate class-balanced label-confident data augmentation. Experiments show ADA-BUD outperforms existing methods on challenging benchmarks, demonstrating its practical potential.