基于频域数据增强的生成对抗网络:双向区分器结构 应对有限的数据
Jian Wei1, Qinzhao Wang1, Zixu Zhao1
1Army Academy of Armored Forces, Beijing, 100071, China.
Heliyon
|February 22, 2024
概括
本研究介绍了FD-GAN,这是一种新型的生成对抗网络 (GAN),旨在使用有限的数据生成稳定的图像. 新方法提高了培训稳定性和图像质量,扩大了GAN应用.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 生成对抗性网络 (GAN) 在图像生成方面表现出色,但通常需要广泛的数据集.
- 有限数据的培训GAN带来了诸如过度装配等挑战,并阻碍了更广泛的应用.
- 现有的数据增强方法通常侧重于像素级别的变化,忽视图像结构和轮.
研究的目的:
- 为有限数据场景开发稳定的GAN培训方法.
- 在数据稀缺的情况下,提高GAN的性能和稳定性.
- 克服传统数据增强技术的局限性.
主要方法:
- 设计了一种新的双分辨器网络架构,以减轻有限数据条件下的过度拟合.
- 提出了一个适应性动态数据增强策略,利用频率域中的拉普拉斯卷积内核.
- 这种频域增强隐式地增加了训练数据,而不会改变像素空间.
主要成果:
- 与现有方法相比,拟议的FD-GAN显示出优越的图像生成能力.
- 该模型获得了令人印象深刻的FID分数:AFHQ-Cat的4.58,AFHQ-Dog的12.007和TankDataSet的10.382.
- 双分辨器和频域增强有效地改善了数据约束下的GAN性能.
结论:
- FD-GAN提供了一个强大的解决方案,用于训练具有有限数据集的GAN.
- 双分辨器和频域增强的组合显著提高了图像生成质量和训练稳定性.
- 这项研究为在数据稀缺环境中更广泛采用GAN铺平了道路.
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