A persistent homology loss enforces controllable connectivity in autoencoder latent spaces, improving one-class classification via kernel density estimation on the learned representations.
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Connectivity-Optimized Representation Learning via Persistent Homology
A persistent homology loss enforces controllable connectivity in autoencoder latent spaces, improving one-class classification via kernel density estimation on the learned representations.