ALDA

2000년 도입 · 논문 2편에서 사용

Adversarial-Learned Loss for Domain Adaptation is a method for domain adaptation that combines adversarial learning with self-training. Specifically, the domain discriminator has to produce different corrected labels for different domains, while the feature generator aims to confuse the domain discriminator. The adversarial process finally leads to a proper confusion matrix on the target domain. In this way, ALDA takes the strengths of domain-adversarial learning and self-training based methods.

출처: Adversarial-Learned Loss for Domain Adaptation

소개 논문: Adversarial-Learned Loss for Domain Adaptation