# Active Adversarial Domain Adaptation

## TL;DR

This paper introduces Active Adversarial Domain Adaptation (AADA), a novel method combining adversarial domain alignment and importance sampling to improve transfer learning across domains, especially with limited target domain labels.

## Contribution

AADA unifies adversarial domain alignment and importance sampling into a single framework for effective domain adaptation with minimal target labels.

## Key findings

- AADA significantly outperforms fine-tuning and sampling methods.
- It maintains advantages even with hundreds of actively annotated examples.
- Effective on challenging tasks like object detection.

## Abstract

We propose an active learning approach for transferring representations across domains. Our approach, active adversarial domain adaptation (AADA), explores a duality between two related problems: adversarial domain alignment and importance sampling for adapting models across domains. The former uses a domain discriminative model to align domains, while the latter utilizes it to weigh samples to account for distribution shifts. Specifically, our importance weight promotes samples with large uncertainty in classification and diversity from labeled examples, thus serves as a sample selection scheme for active learning. We show that these two views can be unified in one framework for domain adaptation and transfer learning when the source domain has many labeled examples while the target domain does not. AADA provides significant improvements over fine-tuning based approaches and other sampling methods when the two domains are closely related. Results on challenging domain adaptation tasks, e.g., object detection, demonstrate that the advantage over baseline approaches is retained even after hundreds of examples being actively annotated.