In this paper, we propose ascalable and learnable non-convex approach for highdimensional RPCA problems, which we call Learned Robust PCA (LRPCA).

The pursuit of high perceptual quality in image restoration has driven the development of revolutionary generative models, capable of producing results often visually indistinguishable from real data. However, as their perceptual quality continues toimprove, these models also exhibit agrowing tendencytogenerate hallucinations -realistic-looking details that do not exist in the ground truth images. Hallucinations in these models create uncertainty about their reliability, raising major concerns about their practical application.

Many machine learning tasks that involve predicting an output response can be solved by training a weighted regression model.

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Recent works have shown that MFLD globally minimizes an entropy-regularized convex functional in the space of measures.

In order to bypass the infinite-dimensional challenge involved in directly computingψ?, we may instead consider afamily ofoptimization problems thatareparametrized byx0.