Year: 2023
Type: conference-paper
Abstract: With the availability of massive labeled training data, powerful machine learning models can be trained. However, the traditional I.I.D. assumption that the training and testing data should follow the same distribution is often violated in reality. While existing domain adaptation approaches can tackle domain shift, it relies on the target samples for training. Domain generalization is a promising technology that aims to train models with good generalization ability to unseen distributions. In this tutorial, we will present the recent advance of domain generalization. Specifically, we introduce the background, formulation, and theory behind this topic. Our primary focus is on the methodology, evaluation, and applications. We hope this tutorial can draw interest of the community and provide a thorough review of this area. Eventually, more robust systems can be built for responsible AI. All tutorial materials and updates can be found online at https://dgresearch.github.io/.
Authors: Jindong Wang, Haoliang Li, Sinno Pan, Xing Xie
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: China Computer Federation
OpenAlex ID: W4321485368