# A review of domain adaptation without target labels

## TL;DR

This review categorizes and analyzes various domain adaptation methods that do not require target labels, focusing on sample, feature, and inference-based approaches to improve cross-domain generalization.

## Contribution

It provides a comprehensive categorization of unsupervised domain adaptation techniques and discusses conditions for bounding cross-domain generalization error.

## Key findings

- Sample-based methods weight observations for target relevance.
- Feature-based methods align source and target feature spaces.
- Inference-based methods incorporate adaptation constraints into parameter estimation.

## Abstract

Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the question: how can a classifier learn from a source domain and generalize to a target domain? We present a categorization of approaches, divided into, what we refer to as, sample-based, feature-based and inference-based methods. Sample-based methods focus on weighting individual observations during training based on their importance to the target domain. Feature-based methods revolve around on mapping, projecting and representing features such that a source classifier performs well on the target domain and inference-based methods incorporate adaptation into the parameter estimation procedure, for instance through constraints on the optimization procedure. Additionally, we review a number of conditions that allow for formulating bounds on the cross-domain generalization error. Our categorization highlights recurring ideas and raises questions important to further research.