嵌入空间分配与角度-规范联合分类器,为少数镜头类增量学习
Dunwei Tu1, Huiyu Yi1, Tieyi Zhang1
1National Key Laboratory for Novel Software Technology, Nanjing University, China; School of Artificial Intelligence, Nanjing University, Nanjing, 210023, China.
概括
简单的班级增量学习 (FSCIL) 代理人适应新班级,只有少数样本. 拟议的SAAN框架平衡具有空间特征,并使用规范差异来改进分类,实现最先进的结果.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 简单的阶级增量学习 (FSCIL) 使代理人能够在保留旧知识的同时,在有限的数据上学习新类.
- 现有的方法在当前类的特征空间占用和培训样本不足方面扎.
- 虚拟类方法和最近类平均值 (NCM) 分类器在处理新类对齐和样本不平衡方面存在局限性.
研究的目的:
- 提出一个新的学习框架,SAAN (空间分配与角度标准联合分类器),以应对FSCIL挑战.
- 为所有类别提供平衡的特征空间分配,并使用规范差异增强分类标准.
- 提高智能代理在动态环境中的适应能力.
主要方法:
- SAAN将功能空间划分为每个学习会话的专用子空间,以预设的类别中心为指导.
- 它为每个类建立了一个规范分布,以生成角度-规范联合逻辑,解决样本不平衡.
- 该框架整合了课堂中心对空间分配和角度规范联合分类器的指导.
主要成果:
- 萨恩在短暂的课堂增量学习任务中取得了最先进的表现.
- 拟议的方法有效地平衡了跨类的功能空间分配.
- 在SAAN中,分类准确度显著提高,特别是在样本不平衡的情况下.
结论:
- 在SAAN框架提供了一个强大的解决方案,为少数射击班级增量学习.
- 它可以无集成为插件模块,以增强现有的最先进的方法.
- SAAN提高了智能代理人适应不断变化的数据分布的能力.
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