CGE-GAN:以对比为导向的进化生成对抗网络,具有动态的适应性重量共享.
Kashif Iqbal1, Xue Yu2, Atifa Rafique1
1School of Computer, Nanjing University of Information Science and Technology, No.219, Ningliu Road, Nanjing, 210044, Jiangsu, China.
概括
对比引导进化GAN (CGE-GAN) 通过增强语义对齐和训练效率来改善图像生成. 这种新的方法可以在降低计算成本的情况下实现高保真度输出.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 生成对抗性网络 (GAN) 在图像合成方面表现出色,但在模式崩,训练不稳定性和架构搜索方面扎.
- 现有的进化GAN缺乏语义对齐,有效重量再利用和代际知识转移.
研究的目的:
- 引入对比引导进化GANs (CGE-GANs) 以实现稳定,高保真的图像生成.
- 解决当前GAN的局限性,包括语义对齐和培训效率.
主要方法:
- 开发了一种混合的瓦瑟斯坦-对比损失函数,用于语义对齐和对抗竞争力.
- 集成的动态适应式重量分配 (DAWS) 进行高效的训练.
- 利用基于蒸的知识交叉来保护跨代的特征.
主要成果:
- CGE-GAN的初始分数 (IS) 达到8.99 (CIFAR-10) 和10.46 (STL-10). CGE-GAN的初始分数 (IS) 达到了8.99 (CIFAR-10) 和10.46 (STL-10). CGE-GAN的初始分数 (IS) 达到了10.46 (STL-10).
- 获得了9.74 (CIFAR-10) 和21.86 (STL-10) 的Fréchet发射距离 (FID).
- 与只有0.36个GPU天的基线相比,FID减少了1.74个点.
结论:
- 对比驱动的进化对于稳定,高准确度的图像生成是有效的.
- 在语义多样性和融合效率方面,CGE-GAN表现出卓越的表现.
- 拟议的方法为推进GANs提供了一个有希望的方向.
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