ReCL:用于增强通用类别发现的插入式模块,使用基于传输的方法来揭示样本中的关系
IEEE transactions on neural networks and learning systems
|August 25, 2025
概括
这项研究引入了一种基于关系的对比学习 (ReCL) 模块,以改进一般化类别发现 (GCD). 通过利用语义关系,ReCL增强了未标记数据的特征歧视,实现了最先进的性能.
科学领域:
- 人工智能
- 机器学习
- 计算机视觉
背景情况:
- 由于培训和测试标签的不同, 深度学习模型在开放式识别中遇到了困难.
- 一般化类别发现 (GCD) 通过同时分类标记和未标记数据来解决这个问题.
- 目前用于GCD的对比学习方法往往忽视未标记数据中的样本间关系.
研究的目的:
- 提出一种新的基于关系的对比学习 (ReCL) 模块,以增强通用类别发现.
- 提高GCD任务中未标记样本的区分特征的学习.
- 将ReCL与现有的GCD模型集成,以提高其性能.
主要方法:
- 开发了基于关系的对比学习 (ReCL) 模块,用于通用类别发现.
- 使用基于运输的分配方法来识别未标记的数据点的语义相关样本.
- 使用基于原型的融合技术创建与未标记数据的对比学习的正.
主要成果:
- 拟议的ReCL模块显著提高了各种现有的GCD模型的性能.
- 不同领域的实验显示了多个基准的最新结果.
- 在ReCL中以运输为基础的样本选择与等号相似性方法相比,产生了语义上相似但又多样化的样本.
结论:
- 基于关系的对比学习模块有效地解决了当前GCD方法的局限性.
- ReCL为现有的GCD框架提供了灵活而强大的补充,增强了它们的功能.
- 该方法通过在未标记的数据中捕获细微的关系来证明卓越的性能.
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