Interpreting U-Nets via Task-Driven Multiscale Dictionary Learning
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U-Nets have been tremendously successful in many imaging inverse problems. In an effort to understand the source of this success, we show that one can reduce a U-Net to a tractable, well-understood sparsity-driven dictionary model while retaining its strong empirical performance. We achieve this by extracting a certain multiscale convolutional dictionary from the standard U-Net. This dictionary imitates the structure of the U-Net in its convolution, scale-separation, and skip connection aspects, while doing away with the nonlinear parts. We show that this model can be trained in a task-driven dictionary learning framework and yield comparable results to standard U-Nets on a number of relevant tasks, including CT and MRI reconstruction. These results suggest that the success of the U-Net may be explained mainly by its multiscale architecture and the induced sparse representation.
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