Empirical comparison finds supervised training yields higher accuracy on convex l1 problems while unsupervised training provides better robustness to distribution shift on nonconvex l0 problems for deep-unfolded ISTA and IHT.
A survey on nonconvex regularization-based sparse and low-rank recovery in signal processing, statistics, and machine learning,
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Comparison between Supervised and Unsupervised Learning in Deep Unfolded Sparse Signal Recovery
Empirical comparison finds supervised training yields higher accuracy on convex l1 problems while unsupervised training provides better robustness to distribution shift on nonconvex l0 problems for deep-unfolded ISTA and IHT.