通过预先训练的模型特征来增强少数镜头的分布外检测
概括
参数高效微调 (PEFT) 卓越于少数镜头的分布外 (OOD) 检测. 新的方法,如不确定性得分组合 (USE) 和域特定和一般知识融合 (DSGF),进一步提高了OOD检测和在分布中的准确性.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 可靠的开放世界智能系统取决于有效的分布外 (OOD) 检测.
- 现有的OOD检测方法在有限的训练样本 (少数镜头场景) 中扎.
研究的目的:
- 开发和评估改进少量射击OOD检测的方法.
- 解决少数射击OOD检测的性能差距,特别是关于预训练模型特征的利用.
主要方法:
- 构建了一个全面的几次射击OOD检测基准.
- 研究的参数高效微调 (PEFT) 技术 (视觉提示调整,视觉适配器调整).
- 提出了一种没有培训的方法,不确定性得分组合 (USE),整合特征匹配得分.
- 开发了域特定和通用知识融合 (DSGF) 以提高几次射击的OOD检测和在分布中的准确性.
主要成果:
- 在几次射击的OOD检测中,PEFT方法的性能优于传统的微调和线性探测.
- USE缩小了传统微调和PEFT之间的绩效差距.
- 在各种微调策略,镜头设置和OOD方法中,DSGF显著提高了少数镜头的OOD检测.
- 此外,DSGF还提高了分销中的准确性.
结论:
- PEFT技术为少数射击的OOD检测提供了一个有希望的方向.
- 像USE和DSGF这样的特征再利用和融合方法可以有效地提高OOD检测性能.
- 拟议的DSGF方法提供了一种可靠的方法,用于几次射击的OOD检测,提高了OOD和分销业绩.
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