用GAN实现有无配对图像间的风格转换，提升生成质量与多样性。
Paired and Unpaired Image to Image Translation using Generative Adversarial Networks
- 采用条件GAN处理配对数据，结合循环一致性损失处理无配对数据。
- 引入精确率、召回率与FID分数评估生成图像质量，结果优于基线。
- 适用于艺术风格迁移、医学图像转换等跨域图像生成场景。
图像到图像的转换是计算机视觉领域的活跃研究方向，能够生成具有不同风格、纹理或分辨率的新图像，同时保持其特征属性。近期架构利用生成对抗网络（GAN）将输入图像从一个领域转换到另一个领域。本文研究了多个图像领域中配对与无配对图像转换任务。针对配对任务，采用条件GAN模型；针对无配对任务，通过循环一致性损失进行训练。实验对比了多种损失函数、多尺度Patch-GAN结构及模型架构。引入新的定量评估指标——精确率、召回率与FID分数。此外，还进行了不同实验结果的定性分析。
原文摘要 · Abstract (English)
Image to image translation is an active area of research in the field of computer vision, enabling the generation of new images with different styles, textures, or resolutions while preserving their characteristic properties. Recent architectures leverage Generative Adversarial Networks (GANs) to transform input images from one domain to another. In this work, we focus on the study of both paired and unpaired image translation across multiple image domains. For the paired task, we used a conditional GAN model, and for the unpaired task, we trained it using cycle consistency loss. We experimented with different types of loss functions, multiple Patch-GAN sizes, and model architectures. New quantitative metrics - precision, recall, and FID score - were used for analysis. In addition, a qualitative study of the results of different experiments was conducted.
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