  Unsupervised model transfer has the potential to greatly improve the
generalizability of deep models to novel domains. Yet the current literature
assumes that the separation of target data into distinct domains is known as a
priori. In this paper, we propose the task of Domain-Agnostic Learning (DAL):
How to transfer knowledge from a labeled source domain to unlabeled data from
arbitrary target domains? To tackle this problem, we devise a novel Deep
Adversarial Disentangled Autoencoder (DADA) capable of disentangling
domain-specific features from class identity. We demonstrate experimentally
that when the target domain labels are unknown, DADA leads to state-of-the-art
performance on several image classification datasets.
