# Open Set Domain Adaptation for Image and Action Recognition

## TL;DR

This paper introduces a versatile open set domain adaptation method that effectively transfers knowledge from labeled datasets to unlabelled target data, handling unknown categories in real-world scenarios for image and action recognition.

## Contribution

It proposes a novel approach for open set domain adaptation that works across various settings and achieves state-of-the-art results on multiple datasets.

## Key findings

- Achieved state-of-the-art results on image classification datasets
- Effective in open set and closed set domain adaptation scenarios
- Applicable to both image and action recognition tasks

## Abstract

Since annotating and curating large datasets is very expensive, there is a need to transfer the knowledge from existing annotated datasets to unlabelled data. Data that is relevant for a specific application, however, usually differs from publicly available datasets since it is sampled from a different domain. While domain adaptation methods compensate for such a domain shift, they assume that all categories in the target domain are known and match the categories in the source domain. Since this assumption is violated under real-world conditions, we propose an approach for open set domain adaptation where the target domain contains instances of categories that are not present in the source domain. The proposed approach achieves state-of-the-art results on various datasets for image classification and action recognition. Since the approach can be used for open set and closed set domain adaptation, as well as unsupervised and semi-supervised domain adaptation, it is a versatile tool for many applications.