PKI:以先前知识为基础的神经网络,用于短暂的班级增量学习
Kexin Bao1, Fanzhao Lin2, Zichen Wang3
1Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100092, China; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, 100049, China.
概括
本研究引入了先验知识注入 (PKI) 神经网络,以应对少量射击类增量学习的挑战. 在学习新类的同时,PKI模型有效地保留了先前的知识,优于现有的方法.
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:
- 短暂的班级增量学习 (FSCIL) 面临着灾难性的遗忘和过度适应新班级的挑战.
- 现有的方法往往会结网络组件,以保存先前的知识,从而可能限制适应.
- 为了有效的增量学习,平衡知识保留和新学习至关重要.
研究的目的:
- 为增强FSCIL提出一个新的先验知识注入神经网络 (PKI).
- 在增量学习期间有效地整合和利用积累的先前知识.
- 为了减轻灾难性的遗忘和过度装配,同时改善新的类识别.
主要方法:
- PKI模型包括一个骨干,一个投影仪集,一个分类器和内存.
- 新的投影仪被添加并与分类器在每个增量会话中进行微调.
- 级联投影仪整合了先前的知识,使新信息的灵活学习成为可能.
主要成果:
- 拟议的PKI方法在识别旧和新类别方面表现出卓越的表现.
- 变种PKIV-1和PKIV-2提供了资源消耗和性能之间的权衡.
- 三个基准的广泛实验表明,PKI的表现优于最先进的FSCIL方法.
结论:
- PKI网络有效地利用先前的知识来进行强有力的,短暂的课堂增量学习.
- 级联投影仪的设计可以灵活地整合新知识.
- 对于面临有限数据的持续学习系统,PKI提供了一个有前途的方向.
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