Open-Set Self-Learning: A Dynamic Approach to Adapt to Changing Data Distributions

This paper proposes an open-set self-learning (OSSL) framework that dynamically adapts to changing data distributions, in contrast to existing methods that learn static and fixed decision boundaries.