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Found 4 papers, 2 papers with code
A Metric for Linear Symmetry-Based Disentanglement
We propose a metric for the evaluation of the level of LSBD that a data representation achieves.
Quantifying and Learning Linear Symmetry-Based Disentanglement
The definition of Linear Symmetry-Based Disentanglement (LSBD) formalizes the notion of linearly disentangled representations, but there is currently no metric to quantify LSBD.
Disentanglement with Hyperspherical Latent Spaces using Diffusion Variational Autoencoders
A disentangled representation of a data set should be capable of recovering the underlying factors that generated it.
Diffusion Variational Autoencoders
A standard Variational Autoencoder, with a Euclidean latent space, is structurally incapable of capturing topological properties of certain datasets.
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