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2018 | OriginalPaper | Buchkapitel

Zero-Shot Deep Domain Adaptation

verfasst von : Kuan-Chuan Peng, Ziyan Wu, Jan Ernst

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

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Abstract

Domain adaptation is an important tool to transfer knowledge about a task (e.g. classification) learned in a source domain to a second, or target domain. Current approaches assume that task-relevant target-domain data is available during training. We demonstrate how to perform domain adaptation when no such task-relevant target-domain data is available. To tackle this issue, we propose zero-shot deep domain adaptation (ZDDA), which uses privileged information from task-irrelevant dual-domain pairs. ZDDA learns a source-domain representation which is not only tailored for the task of interest but also close to the target-domain representation. Therefore, the source-domain task of interest solution (e.g. a classifier for classification tasks) which is jointly trained with the source-domain representation can be applicable to both the source and target representations. Using the MNIST, Fashion-MNIST, NIST, EMNIST, and SUN RGB-D datasets, we show that ZDDA can perform domain adaptation in classification tasks without access to task-relevant target-domain training data. We also extend ZDDA to perform sensor fusion in the SUN RGB-D scene classification task by simulating task-relevant target-domain representations with task-relevant source-domain data. To the best of our knowledge, ZDDA is the first domain adaptation and sensor fusion method which requires no task-relevant target-domain data. The underlying principle is not particular to computer vision data, but should be extensible to other domains.

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Titel: Zero-Shot Deep Domain Adaptation
verfasst von: Kuan-Chuan Peng
Ziyan Wu
Jan Ernst
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2018
Print ISBN: 978-3-030-01251-9

Electronic ISBN: 978-3-030-01252-6

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-01252-6_47

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