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Image Quality Assessment Using Similar Scene as Reference Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Transfer Neural Trees for Heterogeneous Domain Adaptation

Authors : Wei-Yu Chen, Tzu-Ming Harry Hsu, Yao-Hung Hubert Tsai, Yu-Chiang Frank Wang, Ming-Syan Chen

Published in: Computer Vision – ECCV 2016

Publisher: Springer International Publishing

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Abstract

Heterogeneous domain adaptation (HDA) addresses the task of associating data not only across dissimilar domains but also described by different types of features. Inspired by the recent advances of neural networks and deep learning, we propose Transfer Neural Trees (TNT) which jointly solves cross-domain feature mapping, adaptation, and classification in a NN-based architecture. As the prediction layer in TNT, we further propose Transfer Neural Decision Forest (Transfer-NDF), which effectively adapts the neurons in TNT for adaptation by stochastic pruning. Moreover, to address semi-supervised HDA, a unique embedding loss term for preserving prediction and structural consistency between target-domain data is introduced into TNT. Experiments on classification tasks across features, datasets, and modalities successfully verify the effectiveness of our TNT.

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Appendix
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Metadata
Title
Transfer Neural Trees for Heterogeneous Domain Adaptation
Authors
Wei-Yu Chen
Tzu-Ming Harry Hsu
Yao-Hung Hubert Tsai
Yu-Chiang Frank Wang
Ming-Syan Chen
Copyright Year
2016
Book
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46453-4

Electronic ISBN: 978-3-319-46454-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46454-1_25

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