Multi-Attribute Balanced Sampling for Disentangled GAN Controls. (arXiv:2111.00909v2 [cs.LG] CROSS LISTED)

Various controls over the generated data can be extracted from the latent
space of a pre-trained GAN, as it implicitly encodes the semantics of the
training data. The discovered controls allow to vary semantic attributes in the
generated images but usually lead to entangled edits that affect multiple
attributes at the same time. Supervised approaches typically sample and
annotate a collection of latent codes, then train classifiers in the latent
space to identify the controls. Since the data generated by …

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