积极-消极原型融合框架,用于开放集识别
1Department of Computer Science, Changzhi University, Changzhi, 046011, Shanxi, China. 12016596@czc.edu.cn.
Scientific reports
|July 3, 2025
概括
本研究引入了开放集识别 (OSR) 的新框架,以更好地分类已知数据,同时识别未知数据. 提出的方法增强了模型的概括性,并减少了与未知类相关的风险.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 开放集识别 (OSR) 在平衡已知数据的分类准确性与未知数据的识别方面面临着一个关键挑战.
- 现有的方法很难有效地管理与未知数据所在的"开放空间"相关的风险.
研究的目的:
- 开发新的框架,在OSR中改善已知的数据分类和管理未知的数据风险之间的平衡.
- 提高OSR模型的通用化性能,减少开放空间风险.
主要方法:
- 引入了正负原型融合框架 (PNPFF),使用多个正和单个负的原型.
- 开发了使用生成对抗网络 (GAN) 和多重混技术的对抗 (APNPFF) 和增强 (APNPFF++) 版本.
- 这些方法旨在提高类内紧性和类间分离,同时模拟未知的数据特征.
主要成果:
- PNPFF减少了已知数据的分类风险,并通过为未知数据预留空间来部分减轻开放空间风险.
- 通过模拟未知数据,APNPFF和APNPFF++进一步降低了开放空间风险,显著提高了概括性能.
- 跨多个基准数据集的实验证实了拟议方法的有效性和稳定性.
结论:
- 拟议的PNPFF,APNPFF和APNPFF++框架为开放集识别提供了一种优越的方法.
- 这些方法在处理已知和未知的类识别任务方面显著改进.
- 该研究强调了基于原型和生成的方法在推进OSR方面的潜力.
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