APF-GAN: Exploring asymmetric pre-training and fine-tuning strategy for conditional generative adversarial network
Yuxuan Li, Lingfeng Yang, Xiang Li · Computational Visual Media · 2023
The use of generative adversarial network (GAN)based models for the conditional generation of image semantic segmentation has shown promising results in recent years.However, there are still some limitations, including limited diversity of image style, distortion of detailed texture, unbalanced color tone, and lengthy training time.To address these issues, we propose an asymmetric pre-training and fine-tuning (APF)-GAN model.In the pretraining phase, we introduce a progressive growing mechanism for pix2pix conditional GAN frameworks to efficiently generate high-quality images with details.Subsequently, in the fine-tuning phase, we introduce novel semantic spatially-guided noise to improve the robustness of the model and increase style diversity.The proposed algorithm outperformed the high-performance GauGAN model and won the championship of the Second Jittor Artificial Intelligence Challenge.Our model was implemented in the Jittor framework and is available at https:// github.com/zcablii/jittor-Torile-PG_SPADE. APF-GAN Asymmetric pre-training and fine-tuning strategyIn this study, we propose an asymmetric pretraining and fine-tuning strategy for the conditional generative adversarial network (APF-GAN) model