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Bilipschitz Autoencoder Blae

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paper · 2026-04-09
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In this work, we propose the Bi-Lipschitz Autoencoder (BLAE), which introduces two key innovations: (1) an injective regularization scheme based on a separation criterion to eliminate pathological local minima, and (2) a bi-Lipschitz relaxation that preserves geometry and exhibits robustness to data distribution drift.

Bi-Lipschitz Autoencoders for Robust Manifold Learning