L2M

2000년 도입 · 논문 2편에서 사용

L2M is a learning algorithm that can work for most cross-domain distribution matching tasks. It automatically learns the cross-domain distribution matching without relying on hand-crafted priors on the matching loss. Instead, L2M reduces the inductive bias by using a meta-network to learn the distribution matching loss in a data-driven way.

출처: Learning to Match Distributions for Domain Adaptation

소개 논문: Learning to Match Distributions for Domain Adaptation