有条件的相互信息受约束的深度学习用于分类
概括
使用条件相互信息 (CMI) 和标准化CMI (NCMI) 的新深度学习方法提高了分类的准确性和稳定性. 这些技术可以提高深度神经网络 (DNN) 对抗敌对攻击的性能.
科学领域:
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
- 信息理论 信息理论
背景情况:
- 深度神经网络 (DNN) 的性能是使用输出概率分布来评估的.
- 现有的方法缺乏可靠的指标来衡量类内度和类间隔.
研究的目的:
- 引入有条件的相互信息 (CMI) 和规范的CMI (NCMI) 来量化DNN度和分离.
- 建议修改深度学习框架 (CMIC-DL) 来优化这些指标.
主要方法:
- 定义了CMI用于类内度和NCMI用于类间分离.
- 开发了一个CMI受约束深度学习 (CMIC-DL) 框架,采用了交替学习算法.
- 在CIFAR-100和ImageNet数据集上评估了流行的DNN.
主要成果:
- DNN的验证准确性与NCMI值相成比例.
- 在准确性方面,CMIC-DL训练的DNN优于标准的深度学习模型.
- CMIC-DL增强了DNN对抗对方攻击的稳定性.
结论:
- CMI和NCMI为DNN分类性能提供了有效的措施.
- CMIC-DL框架提高了准确性和对抗性稳定性.
- 通过CMI/NCMI可视化学习有助于理解DNN培训动态.
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