A Structured Pruning Algorithm for Model-based Deep Learning

• URL: http://arxiv.org/abs/2311.02003v1
• Date: Fri, 3 Nov 2023 16:05:51 GMT
• Title: A Structured Pruning Algorithm for Model-based Deep Learning
• Authors: Chicago Park, Weijie Gan, Zihao Zou, Yuyang Hu, Zhixin Sun, Ulugbek S. Kamilov
• Abstract summary: We present structured pruning algorithm for model-based deep learning (SPADE) as the first structured pruning algorithm for MBDL networks. We propose three distinct strategies to fine-tune the pruned MBDL networks to minimize the performance loss. Our results highlight that MBDL models pruned by SPADE can achieve substantial speed up in testing time while maintaining competitive performance.
• Score: 8.09765408941809
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There is a growing interest in model-based deep learning (MBDL) for solving imaging inverse problems. MBDL networks can be seen as iterative algorithms that estimate the desired image using a physical measurement model and a learned image prior specified using a convolutional neural net (CNNs). The iterative nature of MBDL networks increases the test-time computational complexity, which limits their applicability in certain large-scale applications. We address this issue by presenting structured pruning algorithm for model-based deep learning (SPADE) as the first structured pruning algorithm for MBDL networks. SPADE reduces the computational complexity of CNNs used within MBDL networks by pruning its non-essential weights. We propose three distinct strategies to fine-tune the pruned MBDL networks to minimize the performance loss. Each fine-tuning strategy has a unique benefit that depends on the presence of a pre-trained model and a high-quality ground truth. We validate SPADE on two distinct inverse problems, namely compressed sensing MRI and image super-resolution. Our results highlight that MBDL models pruned by SPADE can achieve substantial speed up in testing time while maintaining competitive performance.

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