Source Hypothesis Transfer

2000년 도입 · 논문 3편에서 사용

Source Hypothesis Transfer, or SHOT, is a representation learning framework for unsupervised domain adaptation. SHOT freezes the classifier module (hypothesis) of the source model and learns the target-specific feature extraction module by exploiting both information maximization and self-supervised pseudo-labeling to implicitly align representations from the target domains to the source hypothesis.

출처: Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation

소개 논문: Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation