Memory-Efficient Hierarchical Neural Architecture Search for Image Restoration
Haokui Zhang, Ying Li, Hao Chen, Chengrong Gong, Zongwen Bai, Chunhua, Shen

TL;DR
This paper introduces HiNAS, a memory-efficient hierarchical neural architecture search method that automatically designs effective, lightweight neural networks for image denoising and super-resolution, achieving competitive results with reduced search time.
Contribution
The paper proposes a novel hierarchical NAS framework with layer-wise and cell-sharing strategies, enabling fast, memory-efficient search for low-level image restoration networks.
Findings
HiNAS achieves state-of-the-art performance with fewer parameters.
Search process takes only 1-3.5 hours on a single GPU.
Resulting architectures have faster inference speeds.
Abstract
Recently, much attention has been spent on neural architecture search (NAS), aiming to outperform those manually-designed neural architectures on high-level vision recognition tasks. Inspired by the success, here we attempt to leverage NAS techniques to automatically design efficient network architectures for low-level image restoration tasks. In particular, we propose a memory-efficient hierarchical NAS (termed HiNAS) and apply it to two such tasks: image denoising and image super-resolution. HiNAS adopts gradient based search strategies and builds a flexible hierarchical search space, including the inner search space and outer search space. They are in charge of designing cell architectures and deciding cell widths, respectively. For the inner search space, we propose a layer-wise architecture sharing strategy (LWAS), resulting in more flexible architectures and better performance.…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Image and Signal Denoising Methods · Sparse and Compressive Sensing Techniques
