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Type: Conference paper
Title: Deeply learning the messages in message passing inference
Author: Lin, G.
Shen, C.
Reid, I.
Van Den Hengel, A.
Citation: Advances in neural information processing systems, 2015 / Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., Garnett, R. (ed./s), vol.2015-January, pp.361-369
Publisher: Neural Information Processing Systems
Issue Date: 2015
Series/Report no.: Advances in Neural Information Processing Systems
ISBN: 9781510825024
ISSN: 1049-5258
Conference Name: 29th Annual Conference on Neural Information Processing Systems 2015 (NIPS 2015) (7 Dec 2015 - 12 Dec 2015 : Montreal)
Editor: Cortes, C.
Lawrence, N.
Lee, D.
Sugiyama, M.
Garnett, R.
Statement of
Guosheng Lin, Chunhua Shen, Ian Reid, Anton van den Hengel
Abstract: Deep structured output learning shows great promise in tasks like semantic image segmentation. We proffer a new, efficient deep structured model learning scheme, in which we show how deep Convolutional Neural Networks (CNNs) can be used to directly estimate the messages in message passing inference for structured prediction with Conditional Random Fields CRFs). With such CNN message estimators, we obviate the need to learn or evaluate potential functions for message calculation. This confers significant efficiency for learning, since otherwise when performing structured learning for a CRF with CNN potentials it is necessary to undertake expensive inference for every stochastic gradient iteration. The network output dimension of message estimators is the same as the number of classes, rather than exponentially growing in the order of the potentials. Hence it is more scalable for cases that a large number of classes are involved. We apply our method to semantic image segmentation and achieve impressive performance, which demonstrates the effectiveness and usefulness of our CNN message learning method.
Rights: Copyright © (2015) by Neural Information Processing Systems All rights reserved.
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Appears in Collections:Aurora harvest 3
Australian Institute for Machine Learning publications
Computer Science publications

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