Paper Info
Title
β-VAEs can retain label information even at high compression.
Abstract
In this paper, we investigate the degree to which the encoding of a $beta$-VAE captures label information across multiple architectures on Binary Static MNIST and Omniglot. Even though they are trained in a completely unsupervised manner, we demonstrate that a $beta$-VAE can retain a large amount of label information, even when asked to learn a highly compressed representation.
Year
Venue
DocType
2018
arXiv: Learning
Journal
Volume
Citations 
PageRank 
abs/1812.02682
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Emily Fertig142.41
Aryan Arbabi210.70
Alexander A. Alemi3709.92