A Metric for Linear Symmetry-Based Disentanglement

The definition of Linear Symmetry-Based Disentanglement (LSBD) proposed by (Higgins et al., 2018) 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 ful...

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Published inarXiv.org
Main Authors Pérez Rey, Luis A, Tonnaer, Loek, Menkovski, Vlado, Holenderski, Mike, Portegies, Jacobus W
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 26.11.2020
Subjects
Evaluation
Representations
Symmetry
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Abstract The definition of Linear Symmetry-Based Disentanglement (LSBD) proposed by (Higgins et al., 2018) 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. 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 of the data representations obtained for three datasets with underlying \(SO(2)\) symmetries.
AbstractList The definition of Linear Symmetry-Based Disentanglement (LSBD) proposed by (Higgins et al., 2018) 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. 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 of the data representations obtained for three datasets with underlying \(SO(2)\) symmetries.
Author Holenderski, Mike
Pérez Rey, Luis A
Menkovski, Vlado
Tonnaer, Loek
Portegies, Jacobus W
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Representations
Symmetry
Title A Metric for Linear Symmetry-Based Disentanglement
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