Hierarchical Domain Invariant Variational Auto-Encoding with weak domain supervision
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We address the task of domain generalization, where the goal is to train a predictive model based on a number of domains such that it is able to generalize to a new, previously unseen domain. We choose a generative approach within the framework of variational autoencoders and propose a weakly supervised algorithm that is able to account for incomplete and hierarchical domain information. We show that our method is able to learn representations that disentangle domain-specific information from class-label specific information even in complex settings where an unobserved substructure is present in domains. Our interpretable method outperforms previously proposed generative algorithms for domain generalization and achieves competitive performance compared to state-of-the-art approaches, which are based on complex image-processing steps, on the standard domain generalization benchmark dataset PACS.
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