  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.
