Domain Agnostic Learning with Disentangled Representations
This addresses the challenge of improving model generalizability to novel domains in unsupervised transfer learning, which is incremental by building on existing disentanglement methods.
The paper tackles the problem of transferring knowledge from a labeled source domain to unlabeled target domains without prior domain separation, proposing Domain-Agnostic Learning (DAL) and achieving state-of-the-art performance on image classification datasets.
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.