Domain Adaptation under Open Set Label Shift

Understand

We introduce the problem of domain adaptation under Open Set Label Shift (OSLS) where the label distribution can change arbitrarily and a new class may arrive during deployment, but the class-conditional distributions p(x|y) are domain-invariant.

• OSLS subsumes domain adaptation under label shift and Positive-Unlabeled (PU) learning.
• The learner's goals here are two-fold: (a) estimate the target label distribution, including the novel class; and (b) learn a target classifier.
• First, we establish necessary and sufficient conditions for identifying these quantities.