Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionDownload PDF

21 May 2021, 20:41 (edited 21 Jan 2022)NeurIPS 2021 SpotlightReaders: Everyone
  • Keywords: Neural Network Pruning, Lightweight Image Super-Resolution, Aligned Structured Sparsity Learning
  • TL;DR: Optimize image SR networks with network pruning simultaneously and achieve SOTA results
  • Abstract: Lightweight image super-resolution (SR) networks have obtained promising results with moderate model size. Many SR methods have focused on designing lightweight architectures, which neglect to further reduce the redundancy of network parameters. On the other hand, model compression techniques, like neural architecture search and knowledge distillation, typically consume considerable memory and computation resources. In contrast, network pruning is a cheap and effective model compression technique. However, it is hard to be applied to SR networks directly, because filter pruning for residual blocks is well-known tricky. To address the above issues, we propose aligned structured sparsity learning (ASSL), which introduces a weight normalization layer and applies $L_2$ regularization to the scale parameters for sparsity. To align the pruned locations across different layers, we propose a \emph{sparsity structure alignment} penalty term, which minimizes the norm of soft mask gram matrix. We apply aligned structured sparsity learning strategy to train efficient image SR network, named as ASSLN, with smaller model size and lower computation than state-of-the-art methods. We conduct extensive comparisons with lightweight SR networks. Our ASSLN achieves superior performance gains over recent methods quantitatively and visually.
  • Supplementary Material: pdf
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  • Code: https://github.com/MingSun-Tse/ASSL
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