监管适应平衡分布内通用化和分布外检测
IEEE transactions on pattern analysis and machine intelligence
|October 4, 2023
概括
这项研究引入了一种新方法,以提高深度神经网络对外分布 (OOD) 样本的稳定性. 该方法适应了对OOD数据的监督,提高了分销分类和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:
- 深度神经网络表现出分布的脆弱性,经常错误地对分布外 (OOD) 的样本进行了高可靠性分类.
- 目前用于OOD检测的方法包括在培训期间添加OOD样本,这可能导致不可靠的标签和损害在分布 (ID) 分类.
研究的目的:
- 为OOD样本开发一种新的监督适应方法,以提高深度神经网络的稳定性.
- 增强非IID深度学习的分布内概括和分布外检测之间的平衡.
主要方法:
- 测量了ID样本和标签之间的依赖,使用相互信息来表示监督信息.
- 通过二进制回归来研究ID-OOD数据的相关性,以改进监督信息,以便更好地进行类别分离.
主要成果:
- 建议的监管适应方法有效地提高了分销分类准确性.
- 该方法显著提高了检测分布外样本的能力.
- 跨不同数据集和架构的实验验证实了该方法的有效性.
结论:
- 监管适应提供了一个有希望的解决方案,以解决深度学习中的分布式漏洞.
- 这种方法使OOD样本与ID样本更兼容,从而产生更强大的模型.
- 这项工作通过改善OOD耐受性和检测来推进非IID深度学习.
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