Incremental Adversarial Domain Adaptation for Continually Changing Environments

TL;DR

This paper introduces an incremental adversarial domain adaptation method that continuously aligns models to changing environments, improving robustness to appearance shifts like day to night in robotics applications.

Contribution

It proposes a lifelong, incremental adaptation approach using adversarial training and intermediate domains, enhancing performance over large appearance changes.

Findings

Improved handling of large appearance shifts such as day to night.

Incremental adaptation outperforms single-step alignment.

Source domain features can be approximated with a GAN for deployment independence.

Abstract

Continuous appearance shifts such as changes in weather and lighting conditions can impact the performance of deployed machine learning models. While unsupervised domain adaptation aims to address this challenge, current approaches do not utilise the continuity of the occurring shifts. In particular, many robotics 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 intermediate domains which successively diverge from the labelled source domain. We empirically demonstrate that our incremental approach improves handling of large appearance changes, e.g. day to night, on a traversable-path segmentation task compared with a direct, single alignment step approach. Furthermore, by approximating the 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.