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β-VAEs can retain label information even at high compression

Aryan Arbabi


In this paper, we investigate the degree to which the encoding of a β-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 β-VAE can retain a large amount of label information, even when asked to learn a highly compressed representation.

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