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Domain Expansion via Network Adaptation for Solving Inverse Problems
Deep learning-based methods deliver state-of-the-art performance for solving inverse problems that arise in computational imaging.
Factorized Tensor Networks for Multi-Task and Multi-Domain Learning
In this paper, we propose a factorized tensor network (FTN) that can achieve accuracy comparable to independent single-task/domain networks with a small number of additional parameters.
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