Related papers: Explicit Domain Adaptation with Loosely Coupled Samples

Explicit Domain Adaptation with Loosely Coupled Samples

URL: http://arxiv.org/abs/2004.11995v1
Date: Fri, 24 Apr 2020 21:23:45 GMT
Title: Explicit Domain Adaptation with Loosely Coupled Samples
Authors: Oliver Scheel, Loren Schwarz, Nassir Navab, Federico Tombari
Abstract summary: 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.
Score: 85.9511585604837
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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