Compressing Latent Space via Least Volume

arXiv:2404.17773v1 Announce Type: new
Abstract: This paper introduces Least Volume-a simple yet effective regularization inspired by geometric intuition-that can reduce the necessary number of latent dimensions needed by an autoencoder without requiring any prior knowledge of the intrinsic dimensionality of the dataset. We show that the Lipschitz continuity of the decoder is the key to making it work, provide a proof that PCA is just a linear special case of it, and reveal that it has a similar PCA-like importance …

abstract arxiv autoencoder continuity cs.cv cs.lg dataset decoder dimensionality dimensions intrinsic intuition key knowledge least paper prior reduce regularization show simple space the decoder the key type via