This document discusses a method for adapting semantic segmentation models to new domains without labeled target data. It proposes a multi-level adversarial learning approach that transfers knowledge across domains by matching feature and output distributions between source and target data at multiple network levels. This helps generalize models to new, unlabeled images and close the domain gap to improve segmentation performance on different datasets.

1. Learning to Adapt Structured Output
Space for Semantic Segmentation

2. Motivation
• This trained model may not generalize well to
unseen images, especially when there is a
domain gap between the training (source) and
test (target) images.
• To address this issue, knowledge transfer or
domain adaptation techniques have been
proposed to close the gap between source and
target domains, where annotations are not
available in the target domain.

4. Algorithmic Overview

7. Multi-level Adversarial Learning