Bridging Domains with Approximately Shared Features

arXiv:2403.06424v1 Announce Type: cross
Abstract: Multi-source domain adaptation aims to reduce performance degradation when applying machine learning models to unseen domains. A fundamental challenge is devising the optimal strategy for feature selection. Existing literature is somewhat paradoxical: some advocate for learning invariant features from source domains, while others favor more diverse features. To address the challenge, we propose a statistical framework that distinguishes the utilities of features based on the variance of their correlation to label $y$ across domains. Under …

abstract arxiv challenge cs.cv cs.lg diverse domain domain adaptation domains feature features feature selection literature machine machine learning machine learning models performance reduce stat.ml strategy type