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Expert gate: Lifelong learning with a network of experts

Book Contribution - Book Chapter Conference Contribution

© 2017 IEEE. In this paper we introduce a model of lifelong learning, based on a Network of Experts. New tasks / experts are learned and added to the model sequentially, building on what was learned before. To ensure scalability of this process, data from previous tasks cannot be stored and hence is not available when learning a new task. A critical issue in such context, not addressed in the literature so far, relates to the decision which expert to deploy at test time. We introduce a set of gating autoencoders that learn a representation for the task at hand, and, at test time, automatically forward the test sample to the relevant expert. This also brings memory efficiency as only one expert network has to be loaded into memory at any given time. Further, the autoencoders inherently capture the relatedness of one task to another, based on which the most relevant prior model to be used for training a new expert, with fine-tuning or learning-without-forgetting, can be selected. We evaluate our method on image classification and video prediction problems.
Book: Proceedings CVPR 2017
Pages: 3366 - 3375
ISBN:9781538604571
Publication year:2017
BOF-keylabel:yes
IOF-keylabel:yes
Authors from:Higher Education
Accessibility:Closed