微分生成对抗网络架构 搜索以有效注意力和Fréchet距离为指导
Yandan Xu1, Qianjin Zhang1, Yu Xue2
1Huzhou Vocational and Technical College, 299 Xuefu Road, Huzhou 313000, P. R. China.
International journal of neural systems
|January 23, 2026
概括
通过使用高效的注意模块和Fréchet Inception Distance (FID) 指导,EAMGAN改进了生成对抗网络 (GAN) 架构搜索. 与现有技术相比,这种方法提高了图像质量和搜索效率.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 生成对抗网络 (GAN) 架构搜索自动化了高性能网络的设计.
- 像DAMGAN这样的现有可差异化搜索方法面临由于SENet模块效率低下和注意力训练的对抗性损失的局限性,影响搜索效率和架构质量.
研究的目的:
- 提出EAMGAN,一个增强的可分化的GAN架构搜索方法.
- 通过结合高效的注意力和Fréchet Inception Distance (FID) 指导来提高搜索效率和架构质量.
主要方法:
- 引入了EAMGAN,利用轻量级的ECA-Net关注模块来取代SENet的低效组件,以1D卷积来减少参数.
- 从对抗优化中解脱了注意力训练,并采用了基于FID的定制损失函数来直接指导架构选择.
- 在CIFAR-10和STL-10数据集上评估性能.
主要成果:
- 在CIFAR-10上,EAMGAN超过了DAMGAN,获得了更好的初始分数 (IS) 和更低的FID.
- 与DAMGAN的0.09 GPU天相比,通过降低搜索成本 (0.08 GPU天) 获得了优异的结果.
- 在STL-10数据集上证明了竞争性表现和可转移性.
结论:
- EAMGAN为GAN架构搜索提供了更高效和有效的方法.
- 拟议的方法提高了图像生成质量,并减少了计算开销.
- 在生成建模中,EAMGAN显示出对更广泛应用的前景.
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