Please use this identifier to cite or link to this item:
https://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 / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society 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 |
DOI: | 10.1109/CVPR.2017.90 |
Published version: | http://dx.doi.org/10.1109/cvpr.2017.90 |
Appears in Collections: | Aurora harvest 8 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.