对于伪标签校准的递归自信训练在半监督的几次射击学习中
概括
确定性意识递归信心训练 (CARCT) 通过使用信心水平来改进伪标签来改进半监督的几击学习. 这种方法通过在高和低可信度数据上进行递归训练来提高分类器的准确性,直到趋同.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 半监督的短时间学习 (SSFSL) 面临着数据稀缺的挑战.
- 在有限的数据上训练有素的分类器通常会为没有标签的数据产生有偏见的,不准确的伪标签.
- 不准确的伪标签可能会对SSFSL的下游学习任务产生负面影响.
研究的目的:
- 引入一种新的方法,即确定性意识递归信心训练 (CARCT),以提高SSFSL中的伪标签准确性.
- 开发一种技术来选择更具信息性的伪标签数据用于分类器再培训.
- 在数据稀缺的环境中增强分类器的概括能力.
主要方法:
- 卡克特利用伪标签的信心级别来识别用于再培训的信息数据.
- 使用联合双高斯模型来学习半监督的先前信心分布 (ssPCD).
- ssPCD指导了用于递归训练和伪标签校准的高可靠性和低可靠性伪标签数据的选择.
主要成果:
- 与最先进的方法相比,CARCT在广泛的SSFSL实验中表现出卓越的性能.
- 该方法有效地区分使用学习的信心分布的高和低信心伪标签.
- 递归的信心训练导致对未标记的数据进行准确的伪标签,从而增强了分类器的概括性.
结论:
- 通过信任意识的再培训,CARCT成功地解决了SSFSL中偏见的伪标签问题.
- 拟议的ssPCD有效地帮助伪标签校准,提高了分类器的性能.
- 对于数据稀缺的学习场景,CARCT的递归培训机制和自我培训方面提供了一个强大的解决方案.
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