关于坚固而准确的分类器的连续性
IEEE transactions on neural networks and learning systems
|November 11, 2025
概括
机器学习模型的连续性阻碍了对抗攻击的稳定性和准确性. 断续续的假设被提出为创建更可靠,更准确的学习模型的解决方案.
科学领域:
- 机器学习 机器学习
- 理论计算机科学 理论计算机科学
- 人工智能中的稳定性
背景情况:
- 敌对攻击对机器学习模型可靠性构成重大威胁.
- 反对训练提高了稳定性,但通常会降低自然样本的准确性,这表明稳定性和准确性之间的权衡.
- 普遍的观点是,模型的稳定性和准确性本质上是相互矛盾的目标.
研究的目的:
- 挑战坚固性和准确性是不相容的观念.
- 提出一个假设的连续性是实现稳定性和准确性的限制因素.
- 在学习理论中引入一个研究和和全态假设的框架.
主要方法:
- 开发了一个分析和和全态假设的理论框架.
- 进行实证研究,比较连续与不连续假设在常见机器学习任务中的性能.
- 分析了对抗性例子现象,即函数序列连续性和不连续函数的均收之间的冲突.
主要成果:
- 经验证据表明,在某些机器学习任务中,连续假设的表现低于不连续的假设.
- 连续函数努力学习最佳的强硬假设.
- 该研究为对抗性示例现象提供了理论解释.
结论:
- 连续性,而不是强度与准确性,在许多机器学习场景中与最佳性能不相容.
- 不连续的假设对于开发强大而准确的机器学习模型至关重要.
- 这些发现对设计学习规则有实际意义,对理解模型行为和评估有理论意义.
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