进化架构搜索生成对抗网络,使用基于衰老机制的策略
Wenxing Man1, Liming Xu1, Chunlin He1
1School of Computer Science, China West Normal University, Nanchong City 637009, China.
概括
这项研究介绍了EAMGAN,一种对生成对抗网络 (GAN) 的进化神经架构搜索. EAMGAN自动化了GAN设计,提高了训练稳定性和性能,以实现高效的图像生成.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 生成对抗网络 (GAN) 在人工智能中对于图像生成至关重要.
- 传统的GAN由于手动架构设计而遭受训练稳定性问题.
研究的目的:
- 开发一种用于设计稳定和高性能GAN架构的自动化方法.
- 解决传统GAN模型固有的培训稳定性挑战.
主要方法:
- 介绍了对GAN的进化神经架构搜索 (ENAS),命名为EAMGAN.
- 采用了一次性模型自动化GAN架构设计.
- 综合操作重要性指标 (OIM) 和一个稳定性和优化搜索的老化机制.
- 使用非主导排序算法来获得帕雷托最佳解决方案和多样性.
主要成果:
- 在基准数据集上,EAMGAN表现出了竞争力的效率和性能.
- 在CIFAR-10上实现了8.83±0.13的初始得分 (IS) 和9.55的Fréchet初始距离 (FID),仅使用0.66 GPU天.
- 在STL-10,CIFAR-100和ImageNet32数据集中展示了强大的可移植性.
结论:
- EAMGAN有效地自动化了GAN架构的搜索,大大提高了训练稳定性.
- 提出的方法为先进的图像生成提供了高效和高性能的解决方案.
- EAMGAN的架构搜索在各种数据集中展示了广泛的适用性和强大的性能.
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