BackMix:通过删除潜在的前后置先值来规范开放集的识别
概括
本研究介绍了用于开放集识别 (OSR) 的背景混合 (BackMix). 通过将前景与背景脱,BackMix提高了OSR,提高了对未知样本的模型稳定性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 开放集识别 (OSR) 模型必须分类已知的数据,同时识别未知的样本.
- 目前使用辅助数据集的OSR规范化方法对异常值选择敏感.
- 前景背景相关性对OSR性能的影响尚未完全理解.
研究的目的:
- 调查OSR模型是否可以在不依赖精心选择的辅助异常值的情况下得到规范化.
- 探索前景和背景在OSR中的作用.
- 通过解决前后相关性,提出一种改善OSR性能的新方法.
主要方法:
- 在OSR.前景背景相互作用的实证和理论分析.
- 背景混合 (BackMix) 方法的开发:使用类激活地图 (CAM) 和随机背景替换进行前景估计.
- 训练OSR模型使用混合图像来取消前景和背景的相关性.
主要成果:
- 相关背景误导OSR模型,导致部分已知的图像出现故障.
- 不相关的背景作为有效的辅助异常值,帮助规范化.
- BackMix通过删除虚假的前后背景先验来显著提高OSR性能.
结论:
- 将前景与背景脱为强大的OSR至关重要.
- BackMix提供了一种简单的,不依赖推理的,广泛适用的方法来增强OSR.
- 拟议的方法有效地规范了OSR模型,没有敏感异常值选择.
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