June 30, 2022, 1:11 a.m. | Serguei Barannikov, Ilya Trofimov, Grigorii Sotnikov, Ekaterina Trimbach, Alexander Korotin, Alexander Filippov, Evgeny Burnaev

cs.LG updates on arXiv.org arxiv.org

We develop a framework for comparing data manifolds, aimed, in particular,
towards the evaluation of deep generative models. We describe a novel tool,
Cross-Barcode(P,Q), that, given a pair of distributions in a high-dimensional
space, tracks multiscale topology spacial discrepancies between manifolds on
which the distributions are concentrated. Based on the Cross-Barcode, we
introduce the Manifold Topology Divergence score (MTop-Divergence) and apply it
to assess the performance of deep generative models in various domains: images,
3D-shapes, time-series, and on different datasets: …

arxiv data divergence framework lg manifold topology

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