相关实验视频
Updated: May 14, 2025

13:19
Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
8.9K
双 Oracle 神经架构 搜索游戏理论 深度学习模型
概括
本研究引入了一种新的游戏理论方法,用于训练像生成对抗网络 (GAN) 和对抗训练 (AT) 这样的深度学习模型. 双预言框架提高了模型性能和对攻击的稳定性.
科学领域:
- 深度学习 (Deep Learning) 是一种深度学习.
- 游戏理论 游戏理论
- 机器学习 机器学习
背景情况:
- 训练包括生成对抗网络 (GAN) 和对抗训练 (AT) 在内的深度学习模型是复杂的,因为在大型战略空间中找到纳什平衡的挑战.
- 现有的方法往往在这些复杂的培训场景中难以实现可扩展性和融合.
研究的目的:
- 提出一种新的方法来训练使用游戏理论概念的深度学习模型,特别是双预言框架.
- 将双预言框架扩展到对抗神经架构搜索 (NAS) 用于GAN和AT算法.
- 为了提高GAN和AT模型的性能和稳定性.
主要方法:
- 开发了一个双预言框架,利用最佳响应预言,将玩家策略概括为训练模型.
- 采用线性编程来计算元策略,并为可扩展性修剪弱主导策略.
- 应用了框架来开发 DONAS-GAN 和 DONAS-AT 算法.
主要成果:
- 在MNIST,CIFAR-10和TinyImageNet上的实验表明,对DONAS-GAN.有显著的改善.
- 对CIFAR-10,SVHN和TinyImageNet的评估显示,在FGSM和PGD攻击下,DONAS-AT的稳定性得到了提高.
- 与基础架构相比,所有变体在定性和定量指标上都显示出了显著的改进.
结论:
- 拟议的双预言框架有效地提高了GAN和AT模型的培训.
- 这些DONAS-GAN和DONAS-AT变种提供了卓越的性能和坚固性.
- 这种基于游戏理论的方法为复杂的深度学习培训挑战提供了可扩展和有效的解决方案.
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