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Plug-and-Play Methods Provably Converge with Properly Trained Denoisers

ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA

Deep neural networks based on unfolding an iterative algorithm, for example, LISTA (learned iterative shrinkage thresholding algorithm), have been an empirical success for sparse signal recovery. The weights of these neural networks are currently …

Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds

In recent years, unfolding iterative algorithms as neural networks has become an empirical success in solving sparse recovery problems. However, its theoretical understanding is still immature, which prevents us from fully utilizing the power of …

Can We Gain More from Orthogonality Regularizations in Training Deep Networks?