Explicit Domain Adaptation with Loosely Coupled Samples
This work addresses domain adaptation for safety-critical applications like autonomous driving, though it appears incremental as it builds on existing transfer learning methods with a focus on interpretability.
The paper tackles the problem of domain adaptation for autonomous driving by proposing an interpretable transfer learning framework that learns explicit mappings between domains, achieving successful adaptation on image classification and lane change prediction tasks.
Transfer learning is an important field of machine learning in general, and particularly in the context of fully autonomous driving, which needs to be solved simultaneously for many different domains, such as changing weather conditions and country-specific driving behaviors. Traditional transfer learning methods often focus on image data and are black-box models. In this work we propose a transfer learning framework, core of which is learning an explicit mapping between domains. Due to its interpretability, this is beneficial for safety-critical applications, like autonomous driving. We show its general applicability by considering image classification problems and then move on to time-series data, particularly predicting lane changes. In our evaluation we adapt a pre-trained model to a dataset exhibiting different driving and sensory characteristics.