Web: http://arxiv.org/abs/2204.11640

May 6, 2022, 1:12 a.m. | Ziyang Zheng, Wenrui Dai, Duoduo Xue, Chenglin Li, Junni Zou, Hongkai Xiong

cs.LG updates on arXiv.org arxiv.org

It is promising to solve linear inverse problems by unfolding iterative
algorithms (e.g., iterative shrinkage thresholding algorithm (ISTA)) as deep
neural networks (DNNs) with learnable parameters. However, existing ISTA-based
unfolded algorithms restrict the network architectures for iterative updates
with the partial weight coupling structure to guarantee convergence. In this
paper, we propose hybrid ISTA to unfold ISTA with both pre-computed and learned
parameters by incorporating free-form DNNs (i.e., DNNs with arbitrary feasible
and reasonable network architectures), while ensuring theoretical convergence. …

arxiv convergence cv deep free hybrid networks neural neural networks

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