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A Metric for Linear Symmetry-Based Disentanglement
Luis Armando Pérez Rey · Loek Tonnaer · Vlado Menkovski · Mike Holenderski · Jim Portegies

Fri Dec 11 08:30 AM -- 09:30 AM (PST) @

The definition of Linear Symmetry-Based Disentanglement (LSBD) proposed by Higgins et al. outlines the properties that should characterize a disentangled representation that captures the symmetries of data. However, it is not clear how to measure the degree to which a data representation fulfills these properties. In this work, we propose a metric for the evaluation of the level of LSBD that a data representation achieves We provide a practical method to evaluate this metric and use it to evaluate the disentanglement for the data representation obtained for three datasets with underlying SO(2) symmetries.

Author Information

Luis Armando Pérez Rey (Eindhoven University of Technology)
Loek Tonnaer (Eindhoven University of Technology)
Vlado Menkovski (Eindhoven University of Technology)
Mike Holenderski (Eindhoven University of Technology)
Jim Portegies (Eindhoven University of Technology)

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