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Scaling-up Disentanglement for Image Translation
[article]
2021
arXiv
pre-print
Image translation methods typically aim to manipulate a set of labeled attributes (given as supervision at training time e.g. domain label) while leaving the unlabeled attributes intact. Current methods achieve either: (i) disentanglement, which exhibits low visual fidelity and can only be satisfied where the attributes are perfectly uncorrelated. (ii) visually-plausible translations, which are clearly not disentangled. In this work, we propose OverLORD, a single framework for disentangling
arXiv:2103.14017v2
fatcat:r3sinwhijbadvamkpvq7rndgay