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)
├── Universal DA (no prior on label set relationship)
├── Multi-source DA
├── Domain Generalization (no target data at all)
└── Test-time Adaptation (adapt at inference)

Key Methods by Era

Classical Methods

Method	Year	Approach	Key Idea
TCA	2011	Kernel	Transfer Component Analysis
GFK	2012	Subspace	Geodesic Flow Kernel
SA	2013	Subspace	Subspace Alignment
DAN	2015	MMD	Deep Adaptation Networks
DANN	2016	Adversarial	Domain-Adversarial Neural Networks
ADDA	2017	Adversarial	Adversarial Discriminative DA