通过从冗余的LLM语义进行内在学习,实现对未见领域的通用零射击学习
Jiaqi Yue1, Chunhui Zhao1, Jiancheng Zhao1
1State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, China.
概括
本研究介绍了跨领域通用零射击学习 (CDGZSL) 以识别新领域的类. 拟议的元域对齐语义精细化 (MDASR) 方法有效处理域位移和语义信息不对称.
科学领域:
- 计算机科学
- 人工智能
- 机器学习
背景情况:
- 一般化零射击学习 (GZSL) 与域位转移 (domain shift) 发生冲突,其中未见的类被错误地归类为可见的类.
- 现有的GZSL方法仅限于已见域,不涉及跨域概括.
- 信息不对称性来自冗余类语义,特别是在使用大型语言模型 (LLM) 时.
研究的目的:
- 通过引入跨领域通用零射击学习 (CDGZSL) 来解决现有GZSL的局限性.
- 通过构建一个共同的特征空间并获得共享的内在语义,开发一种使GZSL在未见的领域中实现的方法.
- 为了减轻CDGZSL中LLM注释语义引起的信息不对称性.
主要方法:
- 建议对CDGZSL进行元域对齐语义精细化 (MDASR).
- MDASR采用类间相似性对齐来删除基于类间特征关系的非内在语义.
- MDASR利用无形类元生成来保存内在语义,并通过模拟特征生成保持可见和无形类之间的连接.
主要成果:
- MDASR有效地将冗余的语义空间与共同特征空间对齐,减少CDGZSL中的信息不对称.
- 该方法在Office-Home和Mini-DomainNet数据集上显示出有效性.
- 在自建的多域稀有动物数据集上也验证了性能.
结论:
- 提供跨领域通用零射击学习的强大解决方案.
- 这种方法成功地解决了领域转移和语义信息不对称的挑战.
- 基于LLM的共享语义作为CDGZSL未来研究的有价值的基准.
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