归纳式状态-重归属对抗式主动学习与启发式客户群重新调整.
概括
本研究引入了一种诱导性状态重新标记的对抗性主动学习 (AL) 模型 (ISRA),以提高标签效率. 伊斯拉解决了注释不足和采样冗余的问题,优于现有的AL方法.
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:
- 积极学习 (AL) 旨在通过选择最具代表性的样本来降低注释成本.
- 现有的AL方法因注释不足,不可靠的不确定性估计和因忽视内部多样性而导致的抽样冗余而扎.
研究的目的:
- 提出一种诱导性状态重新标记对抗性AL模型 (ISRA),以提高标签效率和样本选择.
- 解决当前AL方法的局限性,包括注释不足和采样冗余.
主要方法:
- 开发了一种诱导性状态重新标记对抗性AL模型 (ISRA),具有统一的表示生成器,诱导性状态重新标记歧视器和启发式集团重新缩放模块.
- 集成的对比学习用于自我监督的培训,利用未标记的数据和相互信息来提高表现质量.
- 设计了一种诱导性不确定性指标,用于状态评分和重新标记未标记的数据,以及一个启发式小组重新缩放模块,用于测量和管理候选样本的内部多样性.
主要成果:
- 与最先进的AL方法相比,ISRA在八个数据集和两个不平衡的场景中表现出优异的性能.
- 该模型通过测量和重新调整候选样本的内部多样性,有效地解决了采样冗余问题.
- 当应用到图像标题的交叉模式AL任务时,实现了卓越的性能.
结论:
- 拟议的ISRA模型为积极学习的标签效率算法提供了显著的进步.
- 伊斯拉有效地克服了AL的关键挑战,从而改善了样本选择和降低了注释成本.
- 该模型的适应性扩展到跨模式任务,展示其多功能性和有效性.
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