Automated Domain Discovery from Multiple Sources to Improve Zero-Shot Generalization

Understand

Domain generalization (DG) methods aim to develop models that generalize to settings where the test distribution is different from the training data.

• In this paper, we focus on the challenging problem of multi-source zero shot DG (MDG), where labeled training data from multiple source domains is available but with no access to data from the target domain.
• A wide range of solutions have been proposed for this problem, including the state-of-the-art multi-domain ensembling approaches.
• Despite these advances, the na\"ive ERM solution of pooling all source data together and training a single classifier is surprisingly effective on standard benchmarks.