# Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising

arXiv: https://arxiv.org/abs/1909.08228 · HTML: https://mlsanity.com/paper/1909.08228/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2019.

## Built on by (most cited first, 16 of 16)

- [Neural Architecture Search: Insights from 1000 Papers](https://mlsanity.com/paper/2301.08727/) (2023)
- [Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution](https://mlsanity.com/paper/2101.06658/) (2021)
- [ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior](https://mlsanity.com/paper/2111.15362/) (2021)
- [Triple-level Model Inferred Collaborative Network Architecture for Video Deraining](https://mlsanity.com/paper/2111.04459/) (2021)
- [Hybrid-Supervised Dual-Search: Leveraging Automatic Learning for Loss-free Multi-Exposure Image Fusion](https://mlsanity.com/paper/2309.01113/) (2023)
- [Multi-scale Attentive Image De-raining Networks via Neural Architecture Search](https://mlsanity.com/paper/2207.00728/) (2022)
- [3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image Classification](https://mlsanity.com/paper/2101.04287/) (2021)
- [Differentiable Neural Architecture Search for Extremely Lightweight Image Super-Resolution](https://mlsanity.com/paper/2105.03939/) (2021)
- [Efficient Automation of Neural Network Design: A Survey on Differentiable Neural Architecture Search](https://mlsanity.com/paper/2304.05405/) (2023)
- [MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising](https://mlsanity.com/paper/2103.12995/) (2021)
- [Multi-Prior Learning via Neural Architecture Search for Blind Face Restoration](https://mlsanity.com/paper/2206.13962/) (2022)
- [Searching Efficient Model-guided Deep Network for Image Denoising](https://mlsanity.com/paper/2104.02525/) (2021)
- [Perceptual Image Restoration with High-Quality Priori and Degradation Learning](https://mlsanity.com/paper/2103.03010/) (2021)
- [FlowNAS: Neural Architecture Search for Optical Flow Estimation](https://mlsanity.com/paper/2207.01271/) (2022)
- [NAS-based Recursive Stage Partial Network (RSPNet) for Light-Weight Semantic Segmentation](https://mlsanity.com/paper/2210.00698/) (2022)
- [Single Cell Training on Architecture Search for Image Denoising](https://mlsanity.com/paper/2212.06368/) (2022)

## Abstract (arXiv)

Recently, neural architecture search (NAS) methods have attracted much attention and outperformed manually designed architectures on a few high-level vision tasks. In this paper, we propose HiNAS (Hierarchical NAS), an effort towards employing NAS to automatically design effective neural network architectures for image denoising. HiNAS adopts gradient based search strategies and employs operations with adaptive receptive field to build an flexible hierarchical search space. During the search stage, HiNAS shares cells across different feature levels to save memory and employ an early stopping strategy to avoid the collapse issue in NAS, and considerably accelerate the search speed. The proposed HiNAS is both memory and computation efficient, which takes only about 4.5 hours for searching using a single GPU. We evaluate the effectiveness of our proposed HiNAS on two different datasets, namely an additive white Gaussian noise dataset BSD500, and a realistic noise dataset SIM1800. Experimental results show that the architecture found by HiNAS has fewer parameters and enjoys a faster inference speed, while achieving highly competitive performance compared with state-of-the-art methods. We also present analysis on the architectures found by NAS. HiNAS also shows good performance on experiments for image de-raining.