MFAGAN: A Compression Framework for Memory-Efficient On-Device Super-Resolution GAN release_66jm5m2q5vaprhirf7weg74u5i

by Wenlong Cheng and Mingbo Zhao and Zhiling Ye and Shuhang Gu

Released as a article .

2021  

Abstract

Generative adversarial networks (GANs) have promoted remarkable advances in single-image super-resolution (SR) by recovering photo-realistic images. However, high memory consumption of GAN-based SR (usually generators) causes performance degradation and more energy consumption, hindering the deployment of GAN-based SR into resource-constricted mobile devices. In this paper, we propose a novel compression framework Multi-scale Feature Aggregation Net based GAN (MFAGAN) for reducing the memory access cost of the generator. First, to overcome the memory explosion of dense connections, we utilize a memory-efficient multi-scale feature aggregation net as the generator. Second, for faster and more stable training, our method introduces the PatchGAN discriminator. Third, to balance the student discriminator and the compressed generator, we distill both the generator and the discriminator. Finally, we perform a hardware-aware neural architecture search (NAS) to find a specialized SubGenerator for the target mobile phone. Benefiting from these improvements, the proposed MFAGAN achieves up to 8.3× memory saving and 42.9× computation reduction, with only minor visual quality degradation, compared with ESRGAN. Empirical studies also show ∼70 milliseconds latency on Qualcomm Snapdragon 865 chipset.
In text/plain format

Archived Files and Locations

application/pdf  2.8 MB
file_n4q6ka3dufat3e6ajvupk6ovry
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2021-07-27
Version   v1
Language   en ?
arXiv  2107.12679v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 1a1ea400-9bfa-4104-8a7c-3ff6d0659613
API URL: JSON