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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