Explicit Domain Adaptation with Loosely Coupled Samples

Scheel O, Schwarz L, Navab N, Tombari F (2020)

Publication Type: Journal article

Publication year: 2020

Journal

IEEE Robotics and Automation Letters IEEE

Book Volume: 5

Pages Range: 6740-6747

Article Number: 9149693

Journal Issue: 4

DOI: 10.1109/LRA.2020.3012127

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.

Involved external institutions

BMW AG - Bayerische Motoren Werke / BMW Group DE Germany (DE) Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Scheel, O., Schwarz, L., Navab, N., & Tombari, F. (2020). Explicit Domain Adaptation with Loosely Coupled Samples. IEEE Robotics and Automation Letters, 5(4), 6740-6747. https://doi.org/10.1109/LRA.2020.3012127

MLA:

Scheel, Oliver, et al. "Explicit Domain Adaptation with Loosely Coupled Samples." IEEE Robotics and Automation Letters 5.4 (2020): 6740-6747.

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