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一个太好了,以免成为事实之前减少捷径依赖的依赖
Nikolay Dagaev1,2, Brett D Roads3, Xiaoliang Luo3
1School of Psychology, HSE University, Moscow, Russia.
Pattern recognition letters
|November 2, 2023
概括
由于依赖快捷方式,深度网络在与分布之外的样本作斗争. 使用低容量网络 (LCNs) 和高容量网络 (HCNs) 的新两阶段方法有效地减少了对捷径的依赖,并改善了通用化.
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:
- 深度神经网络在对象识别方面表现出色,但未能将其推广到分布外 (o.o.d.) 样品. 样品. 这些样品.
- 这种失败往往归因于对表面特征 (快捷方式) 的依赖,而不是不变的,更深层次的概念.
- 现实世界的概念具有复杂的结构,在不同背景下有所不同,挑战简单的解决方案.
研究的目的:
- 提出和评估一种新的方法,以改善深度网络中的分布外通用化.
- 引入"太好以至于不真实"的先验概念,阻止对过于简单的解决方案的依赖.
- 利用低容量网络 (LCN) 作为表面快捷方式的检测器.
主要方法:
- 一种两阶段的培训方法,将低容量网络 (LCN) 和高容量网络 (HCN) 结合起来.
- 利用LCN预测在HCN训练期间识别和减重快捷方式依赖的样本.
- 使用修改后的CIFAR-10数据集,并引入了快捷方式来测试该方法.
主要成果:
- 低容量网络有效地发现了表面的捷径.
- 两阶段的LCN-HCN方法成功地减少了深度网络对快捷方式的依赖.
- 拟议的方法证明了改进的分布外通用化能力.
结论:
- "太好以至于不真实的先验"和LCN-HCN方法为提高模型稳定性提供了一个有希望的方向.
- 通过阻止快捷方式,这种方法鼓励网络学习更不变和更普遍的特征.
- 这一策略对于开发可靠地在各种现实条件下运行的AI系统至关重要.
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