Incremental Adversarial Domain Adaptation

Continuous appearance shifts such as changes in weather and lighting conditions can impact the performance of deployed machine learning models. Unsupervised domain adaptation aims to address this challenge, though current approaches do not utilise the continuity of the occurring shifts. Many robotic applications exhibit these conditions and thus facilitate the potential to incrementally adapt a learnt model over minor shifts which integrate to massive differences over time. Our work presents an adversarial approach for lifelong, incremental domain adaptation which benefits from unsupervised alignment to a series of sub-domains which successively diverge from the labelled source domain. We demonstrate on a drivable-path segmentation task that our incremental approach can better handle large appearance changes, e.g. day to night, compared with a prior single alignment step approach. Furthermore, by approximating the marginal feature distribution for the source domain with a generative adversarial network, the deployment module can be rendered fully independent of retaining potentially large amounts of the related source training data for only a minor reduction in performance.