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通过灵活的超级网络搜索精确和强大的神经架构
IEEE transactions on neural networks and learning systems
|January 6, 2026
概括
在超级网络中,ARNAS++通过引入灵活的参数预算和宽度来增强神经架构搜索 (NAS). 该方法提高了自然准确性和对抗性稳定性,优于对基准数据集的现有方法.
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:
- 神经架构搜索 (NAS) 设计准确的模型,但通常会导致架构易受对抗性攻击的攻击.
- 现有的强大的NAS方法在固定的超级网络中进行优化,限制了灵活性和对抗性强度.
研究的目的:
- 开发一种新的方法,ARNAS++,用于搜索准确和强大的神经架构.
- 通过结合可适应的参数预算和网络宽度来增强NAS的灵活性.
主要方法:
- 引入了参数预算控制损失,以减少后来的网络单元中的参数,提高对抗性稳定性.
- 开发了一种可学习的过器数量减少比率,用于灵活的超网过器控制,从而可以搜索更强大的架构.
- 根据最先进的方法对六个基准数据集进行ARNAS++评估.
主要成果:
- 与现有方法相比,ARNAS++在自然准确性和对抗性稳定性方面表现出卓越的性能.
- 废弃性研究证实了拟议的参数预算控制和过器减少比率的有效性.
结论:
- ARNAS++提供了一种灵活有效的方法来设计神经架构,以提高对抗攻击的准确性和稳定性.
- 该方法在强大的神经架构搜索中取得了重大进展.
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