Open Set Domain Adaptation Under Distribution Shift

This presentation delves into Open-Set Domain Adaptation challenges in real-world Machine Learning, highlighting the innovative CoLOR method addressing background distribution shifts effectively. It elaborates on key learning rules, domain discrimination, and advantages with overparameterized models. Backed by experimental outcomes across diverse datasets and metrics, the study offers valuable insights comparing CoLOR with standard baselines. The discussion concludes with future research...