$β$-VAEs can retain label information even at high compression
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.
PDF AbstractDatasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here