A Design Space Study for LISTA and Beyond

• URL: http://arxiv.org/abs/2104.04110v1
• Date: Thu, 8 Apr 2021 23:01:52 GMT
• Title: A Design Space Study for LISTA and Beyond
• Authors: Tianjian Meng, Xiaohan Chen, Yifan Jiang, Zhangyang Wang
• Abstract summary: In recent years, great success has been witnessed in building problem-specific deep networks from unrolling iterative algorithms. This paper revisits the role of unrolling as a design approach for deep networks, to what extent its resulting special architecture is superior, and can we find better? Using LISTA for sparse recovery as a representative example, we conduct the first thorough design space study for the unrolled models.
• Score: 79.76740811464597
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, great success has been witnessed in building problem-specific deep networks from unrolling iterative algorithms, for solving inverse problems and beyond. Unrolling is believed to incorporate the model-based prior with the learning capacity of deep learning. This paper revisits the role of unrolling as a design approach for deep networks: to what extent its resulting special architecture is superior, and can we find better? Using LISTA for sparse recovery as a representative example, we conduct the first thorough design space study for the unrolled models. Among all possible variations, we focus on extensively varying the connectivity patterns and neuron types, leading to a gigantic design space arising from LISTA. To efficiently explore this space and identify top performers, we leverage the emerging tool of neural architecture search (NAS). We carefully examine the searched top architectures in a number of settings, and are able to discover networks that are consistently better than LISTA. We further present more visualization and analysis to "open the black box", and find that the searched top architectures demonstrate highly consistent and potentially transferable patterns. We hope our study to spark more reflections and explorations on how to better mingle model-based optimization prior and data-driven learning.

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