Domain Adaptation Papers Guide

Overview

Domain adaptation addresses the problem of training models on one data distribution (source domain) and deploying them on a different distribution (target domain). This curated collection covers the full spectrum — from unsupervised domain adaptation (UDA) and domain generalization to partial, open-set, and source-free adaptation. Organized by methodology and application area with regularly updated paper lists.

Taxonomy of Methods