OT Score: An OT based Confidence Score for Prototype-Assisted Source Free Unsupervised Domain Adaptation

ArXi:2505.11669v3 Announce Type: replace-cross We address the computational and theoretical limitations of current distributional alignment methods for source-free unsupervised domain adaptation (SFUDA) using source class-mean features. In particular, we focus on estimating classification performance and confidence in the absence of target labels. Current theoretical frameworks for these methods often yield computationally intractable quantities and fail to adequately reflect the properties of the alignment algorithms employed. To overcome these challenges, we.