UGC: Unified GAN Compression for Efficient Image-to-Image Translation
- URL: http://arxiv.org/abs/2309.09310v1
- Date: Sun, 17 Sep 2023 15:55:09 GMT
- Title: UGC: Unified GAN Compression for Efficient Image-to-Image Translation
- Authors: Yuxi Ren, Jie Wu, Peng Zhang, Manlin Zhang, Xuefeng Xiao, Qian He, Rui
Wang, Min Zheng, Xin Pan
- Abstract summary: We propose a new learning paradigm, Unified GAN Compression (UGC), with a unified objective to seamlessly prompt the synergy of model-efficient and label-efficient learning.
We formulate a heterogeneous mutual learning scheme to obtain an architecture-flexible, label-efficient and performance-excellent model.
- Score: 20.3126581529643
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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