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LRNAS:对相对强大的轻量级神经架构进行差异化搜索
概括
本研究介绍了一种轻量级和强大的神经架构搜索 (LRNAS) 方法. 在没有人工设计的情况下,LRNAS自动发现高效的深度神经网络,这些神经网络是准确的,并且能够抵御敌对攻击.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 深度神经网络 (DNN) 需要对抗性强度才能实现可靠的部署.
- 提高对抗性稳定性往往会导致网络尺寸增加,从而产生权衡.
- 目前的方法结合模型压缩和对抗训练,严重依赖于手动神经架构设计.
研究的目的:
- 提出一种轻量级且强大的神经架构搜索 (LRNAS) 方法.
- 自动发现神经网络架构,既轻量级,又对抗性强大.
- 克服手动神经架构设计在实现对抗强度和效率方面的局限性.
主要方法:
- 开发了一种新的搜索策略,以量化搜索空间中的组件贡献.
- 实施了一种贪的策略,用于建筑选择,以保持模型大小,同时结合有益的组件.
- 采用自动化神经架构搜索来识别最佳的轻量级和强大的模型.
主要成果:
- 拟议的LRNAS方法成功识别了具有高自然精度和对抗性强度的轻量级神经架构.
- 对基准数据集的实验结果表明,对抗对抗攻击的最先进方法的优越性.
- 废弃性研究证实了单个LRNAS组件在提高整体性能方面的有效性.
结论:
- 在搜索架构中,LRNAS有效地保证了轻度,自然准确性和对抗性强度.
- 该方法可以自动设计高效和安全的深度神经网络.
- LRNAS为开发实用且有弹性的DNN提供了一个有希望的方向.
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