对比的开放式基于主动学习的样本选择,用于图像分类
概括
本研究引入了开放式主动学习 (AL) 的新方法,以有效地选择信息化的分布式 (ID) 样本,同时避免分布式 (OOD) 之外的数据. 该方法增强了表示学习,并取得了最先进的结果.
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) 通常假设所有未标记的数据都是分布式 (ID).
- 开放式的AL场景包括ID和OOD样本在未标记的数据中.
- 标准AL方法由于选择不确定的OD样本而失败,浪费计算资源并降低模型性能.
研究的目的:
- 开发一个有效的主动学习策略,用于混合ID和OOD未标记数据的开放场景.
- 改进信息化ID样本的选择,同时减轻OOD样本的错误分类.
- 在AL框架内增强分类器的表示学习能力.
主要方法:
- 引入了两个新标准:对比的信心 (ID可能性) 和历史差异 (样本硬度).
- 开发了一个对比的集群框架,将OOD检测集成到分类器中.
- 平衡的对比性信心和历史差异,以优先考虑信息性的ID样本.
主要成果:
- 提出的方法成功地识别并避免选择OOD样本.
- 在开放式主动学习的几个基准数据集上实现了最先进的性能.
- 增强了网络的表示学习,而不需要单独的OOD检测模块.
结论:
- 这种新的方法有效地解决了开放式主动学习的挑战.
- 对比的集群框架为样本选择和OOD检测提供了统一的解决方案.
- 拟议的方法在复杂的AL场景中表现出卓越的性能和效率.
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