在域内GAN倒置用于忠实重建和可编辑性
IEEE transactions on pattern analysis and machine intelligence
|February 1, 2024
概括
本研究介绍了域内生成对抗网络 (GAN) 倒置,以实现更好的图像编辑. 该方法确保反向隐藏代码支持语义操纵,增强GANs.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 生成对抗网络 (GAN) 在图像合成方面表现出色,但在真正的图像编辑方面却很难.
- GAN反转,寻找图像恢复的潜在代码,通常优先考虑像素级重建而不是语义编辑功能.
研究的目的:
- 解决现有的GAN逆转技术在支持语义图像操纵方面的局限性.
- 提出一种新的域内GAN反转方法,以提高反转图像的编辑性.
主要方法:
- 开发了一个域内GAN反转方法,使用域导向编码器和域规范化优化器.
- 在预训练的GAN的本地潜伏空间内规范化的反转潜伏代码.
- 对编码器结构,起点和参数空间进行了全面的分析.
主要成果:
- 拟议的方法有效地重用GAN知识进行图像重建,使各种编辑应用程序无需重新培训.
- 展示了图像重建质量和语义编辑能力之间的权衡.
- 提供了关于GAN如何在它们的潜伏分布中编码图像语义的见解.
结论:
- 在域内GAN倒置显著提高了GAN倒置图像的语义可编辑性.
- 该方法通过更有效地利用预先训练的GAN来促进实际的图像编辑应用.
- 了解重建-编辑权衡对于优化GAN隐性空间表示至关重要.
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