February 23, 2024Open Access

A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data

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Wasserstein autoencoders learn underlying data distributions with convergence rates governed purely by intrinsic dimension rather than ambient space.
Theoretical convergence rates of expected excess risk scale with sample size and intrinsic dimension, bypassing ambient high-dimensional feature spaces.
Supports principled deployment of variational autoencoders on complex natural images, confirming network architecture choices ensure statistical efficiency.

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Implication

Theoretical analysis demonstrates Wasserstein autoencoders achieve convergence rates tied to intrinsic data dimension, indicating efficient distribution learning in high-dimensional feature spaces.

Cite This Study

Chakraborty et al. (2024) studied this question.

synapsesocial.com/papers/68e77f50b6db6435876f2f62 https://doi.org/10.48550/arxiv.2402.15710

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Implication

Theoretical analysis demonstrates Wasserstein autoencoders achieve convergence rates tied to intrinsic data dimension, indicating efficient distribution learning in high-dimensional feature spaces.

A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data

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Discussion

Implication

Cite This Study

A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data

Key Points

Discussion

Implication

Cite This Study

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