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Compromise as a Way to Promote Convention Emergence and to Reduce Social Unfairness in Multi-Agent Systems Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Multiple-Task Learning and Knowledge Transfer Using Generative Adversarial Capsule Nets

Authors : Ancheng Lin, Jun Li, Lujuan Zhang, Zhenyuan Ma, Weiqi Luo

Published in: AI 2018: Advances in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

It is common that practical data has multiple attributes of interest. For example, a picture can be characterized in terms of its content, e.g. the categories of the objects in the picture, and in the meanwhile the image style such as photo-realistic or artistic is also relevant. This work is motivated by taking advantage of all available sources of information about the data, including those not directly related to the target of analytics.
We propose an explicit and effective knowledge representation and transfer architecture for image analytics by employing Capsules for deep neural network training based on the generative adversarial nets (GAN). The adversarial scheme help discover capsule-representation of data with different semantic meanings in respective dimensions of the capsules. The data representation includes one subset of variables that are particularly specialized for the target task – by eliminating information about the irrelevant aspects. We theoretically show the elimination by mixing conditional distributions of the represented data. Empirical evaluations show the propose method is effective for both standard transfer-domain recognition tasks and zero-shot transfer.

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Metadata
Title
Multiple-Task Learning and Knowledge Transfer Using Generative Adversarial Capsule Nets
Authors
Ancheng Lin
Jun Li
Lujuan Zhang
Zhenyuan Ma
Weiqi Luo
Copyright Year
2018
Book
AI 2018: Advances in Artificial Intelligence

Print ISBN: 978-3-030-03990-5

Electronic ISBN: 978-3-030-03991-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-03991-2_60

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