# Unsupervised Domain Adaptation via Regularized Conditional Alignment

## TL;DR

This paper introduces a novel unsupervised domain adaptation method that aligns joint input-output distributions using regularization and adversarial training, improving classifier performance across domains.

## Contribution

It presents a new joint distribution alignment technique with a regularized objective and adversarial regularization, enhancing domain adaptation effectiveness.

## Key findings

- Effective joint distribution alignment improves domain invariance.
- Regularization enforces disjoint class-conditional supports.
- Adversarial training boosts classifier performance on unlabeled domains.

## Abstract

We propose a method for unsupervised domain adaptation that trains a shared embedding to align the joint distributions of inputs (domain) and outputs (classes), making any classifier agnostic to the domain. Joint alignment ensures that not only the marginal distributions of the domain are aligned, but the labels as well. We propose a novel objective function that encourages the class-conditional distributions to have disjoint support in feature space. We further exploit adversarial regularization to improve the performance of the classifier on the domain for which no annotated data is available.