Enhancing Continuous Domain Adaptation with Multi-Path Transfer Curriculum

arXiv:2402.16681v2 Announce Type: replace
Abstract: Addressing the large distribution gap between training and testing data has long been a challenge in machine learning, giving rise to fields such as transfer learning and domain adaptation. Recently, Continuous Domain Adaptation (CDA) has emerged as an effective technique, closing this gap by utilizing a series of intermediate domains. This paper contributes a novel CDA method, W-MPOT, which rigorously addresses the domain ordering and error accumulation problems overlooked by previous studies. Specifically, we construct …

abstract arxiv challenge continuous cs.lg curriculum data distribution domain domain adaptation fields gap giving machine machine learning path series testing training transfer transfer learning type