Recent years have witnessed the prevailing progress of Generative Adversarial Networks (GANs) in image-to-image translation. However, the success of these GAN models hinges on ponderous computational costs and labor-expensive training data. Current efficient GAN learning techniques often fall into two orthogonal aspects: i) model slimming via reduced calculation costs; ii)data/label-efficient learning with fewer training data/labels. To combine the best of both worlds, we propose a new learning paradigm, Unified GAN Compression (UGC), with a unified optimization objective to seamlessly prompt the synergy of model-efficient and label-efficient learning. UGC sets up semi-supervised-driven network architecture search and adaptive online semi-supervised distillation stages sequentially, which formulates a heterogeneous mutual learning scheme to obtain an architecture-flexible, label-efficient, and performance-excellent model.
@article{arxiv.2309.09310,
title = {UGC: Unified GAN Compression for Efficient Image-to-Image Translation},
author = {Yuxi Ren and Jie Wu and Peng Zhang and Manlin Zhang and Xuefeng Xiao and Qian He and Rui Wang and Min Zheng and Xin Pan},
journal= {arXiv preprint arXiv:2309.09310},
year = {2023}
}