UIDAPLE: Unsupervised Incremental Domain Adaptation through Adaptive Prompt Learning

Abstract

Continual learning poses significant challenges for deep neural networks, notably catastrophic forgetting, particularly when faced with shifting data distributions that compromise previously acquired knowledge. This paper tackles these issues within the Unsupervised Incremental Domain Adaptation (UIDA) framework, where the initial source domain is labeled, but subsequent domains are not. Existing methods often struggle with limited cross-domain generalization and adaptation capabilities. As a remedy, we introduce UIDAPLE, a novel approach that utilizes a unified prompt across all domains, leveraging the foundation model CLIP to obviate the need for isolated domain treatments. Specifically, UIDAPLE implements supervised prompt learning in the labeled source domain and extends this learning to unlabeled domains through confidence-based adaptation. We also present an efficient parameter alignment strategy that maintains semantic coherence across domains, effectively balancing stability and plasticity to combat catastrophic forgetting. Extensive evaluations on two benchmark datasets reveal that UIDAPLE markedly surpasses other UIDA techniques in performance.