通过双边分布得分来提高分布之外的检测
Bolun Zheng1, Yuhao Lin1, Yao Zhu2
1School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China.
概括
本研究介绍了双边分布分数 (BDS) 用于在机器学习中改进分布外 (OOD) 检测. 通过有效识别无需重新训练模型的OOD样本,BDS提高了可信度.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 在安全关键系统中可靠的人工智能,OOD检测至关重要.
- 现有的方法与分类器过度自信和欺骗性的OOD样本作斗争.
- 当前的后期OOD检测方法缺乏针对某些OOD数据分布的强大性能.
研究的目的:
- 开发一种新的OOD检测方法,克服现有方法的局限性.
- 引入一个新的OOD评分机制,基于"理想的OOD样本"的概念.
- 提高机器学习模型在现实应用中的可靠性和安全性.
主要方法:
- 提出了"理想的OOD样本"的概念,在特征空间中与所有类中心保持同等距离.
- 根据与这个理想的OOD样本的相似性定义了一个新的OOD得分.
- 引入了双边分销得分 (BDS),整合了OOD和在分销 (ID) 的得分.
主要成果:
- 在ImageNet-1k和CIFAR-10基准测试中,BDS展示了优越的OOD检测能力.
- 与最先进的方法相比,在95% (FPR95) 的平均假阳性率下降了10.78%.
- 该方法不需要进行架构修改或重新培训,确保向后兼容.
结论:
- BDS在OOD检测方面取得了重大进展,提高了模型可信度.
- 提出的方法是有效和实用的,与现有技术无整合.
- BDS提高了机器学习模型与OOD样本的稳定性.
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