Incremental Adversarial Domain Adaptation for Continually Changing Environments

Appearance changes based on lighting, seasonal, and weather conditions provide a significant challenge for outdoor robots relying on machine learning models for perception. While providing high performance in their training domain, visual shifts occurring in the environment can result in significant deviations from the training distribution, severely reducing accuracy during deployment. Commonly, this challenge is partially counteracted by employing additional training methods to render these models invariant to their application domain [1]. For scenarios where labelled data is unavailable in the target domain, the problem can be addressed in the context of unsupervised domain adaptation [2], [3]. Recent stateof-the-art approaches which address this challenge operate by training deep neural networks within an adversarial domain adaptation (ADA) framework. These approaches are characterised by the optimisation of potentially multiple encoders with the objective to confuse a domain discriminator operating on their output [3], [4], [5] in additional to their main objective. The main intuition behind this framework is that by training the encoder to obtain a domain invariant embedding, we allow the main supervised task to be robust to changes in the application domain.