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 stateof-the-art performance on several image classification datasets.
Compared to Domain Adaptation, Domain Agnostic Learning aims to learn from a source domain and map to arbitrary target domains instead of one specific known target domain REF .
Domain Agnostic Learning with Disentangled Representations
Proceedings of the 36th International Conference on Machine Learning, Long Beach, California, PMLR 97, 2019
Computer Science