Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/115993
Citations
Scopus Web of Science® Altmetric
?
?
Type: Conference paper
Title: Infinite variational autoencoder for semi-supervised learning
Author: Abbasnejad, M.
Dick, A.
van den Hengel, A.
Citation: Proceedings: 30th IEEE Conference on Computer Vision and Pattern Recognition, 2017 / vol.2017-January, pp.781-790
Publisher: IEEE
Issue Date: 2017
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 9781538604571
ISSN: 1063-6919
Conference Name: 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) (21 Jul 2017 - 26 Jul 2017 : Honolulu)
Statement of
Responsibility: 
M. Ehsan Abbasnejad, Anthony Dick, Anton van den Hengel
Abstract: This paper presents an infinite variational autoencoder (VAE) whose capacity adapts to suit the input data. This is achieved using a mixture model where the mixing coefficients are modeled by a Dirichlet process, allowing us to integrate over the coefficients when performing inference. Critically, this then allows us to automatically vary the number of autoencoders in the mixture based on the data. Experiments show the flexibility of our method, particularly for semi-supervised learning, where only a small number of training samples are available.
Rights: © 2017 IEEE
RMID: 0030082774
DOI: 10.1109/CVPR.2017.90
Published version: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8097368
Appears in Collections:Australian Institute for Machine Learning publications
Computer Science publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.