通过GAN先前蒸进行少数拍摄面部造型
IEEE transactions on neural networks and learning systems
|March 27, 2024
概括
GAN先蒸 (GPD) 有效地解决了几次拍摄的面部造型挑战. 这种方法可以在有限的数据基础上加强训练,与现有技术相比,可以获得更好的结果.
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:
- 最近面部造型的进步在训练数据有限时,由于显著的性能下降而受到阻碍.
- 现有的面部造型的少数射击学习方法往往无法满足非常小的数据集 (少于10个样本) 的严格要求,或产生低于最佳的结果.
研究的目的:
- 提出GAN先蒸 (GPD),一种有效的方法,用于几次拍摄的面部造型.
- 即使使用极其有限的目标域数据,也可以实现强大的面部造型.
主要方法:
- 开发了两种模型系统:一个教师网络与Generative Adversarial Network (GAN) Prior和一个学生网络进行端到端的翻译.
- 使用最小的样本,将一个大规模的预培训教师网络调整为目标领域.
- 通过从共享隐藏代码生成源和目标域图像来实现少量拍摄数据增强.
- 引入了基于的知识蒸模块,将知识从教师转移到学生网络,利用增强数据的差异.
主要成果:
- 经过培训的学生网络通过有效地吸收蒸知识,展示了出色的概括能力.
- 定性和定量实验证实了与最先进的方法相比,在几次拍摄的面部造型场景中表现优越.
- 即使在很少的训练样本中,也可以获得高质量的造型结果.
结论:
- GAN先蒸 (GPD) 提供了一种强大的解决方案,用于几次拍摄的面部造型.
- 建议的知识蒸方法有效地转移知识,使有限的数据能够提供强大的性能.
- 在需要短时间学习的场景中,GPD显著优于现有的方法,用于面部图像翻译.
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