Towards Context-Aware Domain Generalization: Understanding the Benefits and Limits of Marginal Transfer Learning

arXiv:2312.10107v2 Announce Type: replace
Abstract: In this work, we analyze the conditions under which information about the context of an input $X$ can improve the predictions of deep learning models in new domains. Following work in marginal transfer learning in Domain Generalization (DG), we formalize the notion of context as a permutation-invariant representation of a set of data points that originate from the same domain as the input itself. We offer a theoretical analysis of the conditions under which this …

abstract analyze arxiv benefits context cs.ai cs.lg deep learning domain domains information notion predictions transfer transfer learning type understanding work