Robust PCA is a standard tool for learning a linear subspace in the presence of sparse corruption or rare outliers. What about robustly learning manifolds that are more realistic models for natural data, such as images? There have been several recent attempts to generalize robust PCA to manifold settings. In this paper, we propose ℓ1- and scaling-invariant ℓ1/ℓ2-robust autoencoders based on a surprisingly compact formulation built on the intuition that deep autoencoders perform manifold learning. We demonstrate on several standard image datasets that the proposed formulation significantly outperforms all previous methods in collectively removing sparse corruption, without clean images for training. Moreover, we also show that the learned manifold structures can be generalized to unseen data samples effectively.
@article{arxiv.2303.02828,
title = {Robust Autoencoders for Collective Corruption Removal},
author = {Taihui Li and Hengkang Wang and Peng Le and XianE Tang and Ju Sun},
journal= {arXiv preprint arXiv:2303.02828},
year = {2023}
}