Samenvatting
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.
Originele taal-2 | Engels |
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Status | Gepubliceerd - 26 nov. 2020 |
Evenement | NeurIPS 2020 workshop on Differential Geometry meets Deep Learning - Online Duur: 11 dec. 2020 → 11 dec. 2020 https://sites.google.com/view/diffgeo4dl/call-for-papers?authuser=0 |
Workshop
Workshop | NeurIPS 2020 workshop on Differential Geometry meets Deep Learning |
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Verkorte titel | DiffGeo4DL |
Periode | 11/12/20 → 11/12/20 |
Internet adres |