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

Domain Adaptation
├── Unsupervised DA (UDA)
│   ├── Discrepancy-based (MMD, CORAL, CDD)
│   ├── Adversarial-based (DANN, ADDA, CDAN)
│   ├── Reconstruction-based (DRCN, DSN)
│   └── Self-training (SHOT, CBST)
├── Semi-supervised DA
├── Source-free DA (no source data at adaptation time)
├── Partial DA (target has subset of source classes)
├── Open-set DA (target has unknown classes)