修剪的平均值:改善分布外检测的性能和稳定性
IEEE transactions on neural networks and learning systems
|March 24, 2025
概括
在训练期间,我们引入了平均修剪 (AoP) 来稳定神经网络分布外 (OOD) 检测. 这种方法结合了模型平均和修剪,以提高性能并减少不稳定性,以实现可靠的OOD检测.
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:
- 在现实场景中,分布外 (OOD) 检测对于安全部署神经网络至关重要.
- 在培训期间OOD检测方法的稳定性和性能仍未得到充分探索.
- 现有的方法往往表现出不稳定的行为,特别是当训练接近完成或训练错误最小时.
研究的目的:
- 调查神经网络训练期间OOD检测性能的不稳定性.
- 提出一种新的方法,即修剪平均值 (AoP),以解决这些不稳定性.
- 提高OOD检测系统的可靠性和稳定性.
主要方法:
- 拟议的修剪平均值 (AoP),集成模型平均值 (MA) 和修剪技术.
- 模型平均 (MA) 用于平滑优化环境并稳定性能.
- 修剪被用来通过删除多余的特征来消除过度拟合,从而增强概括性.
主要成果:
- 证明OOD检测性能随着接近零的训练错误而下降,并在训练后期呈现急剧变化.
- 展示了AoP在缓解培训不稳定性和提高OOD检测性能方面的有效性.
- 在各种数据集和神经网络架构中验证了该方法的有效性.
结论:
- 修剪的平均值 (AoP) 有效地稳定和增强神经网络中的分布外检测.
- 模型平均和修剪的组合提供了一个强大的解决方案,以培训诱导的不稳定性.
- AoP为在开放世界环境中部署更可靠的AI系统提供了一个有希望的方法.
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