在图像分类中增强瓶概念学习
Xingfu Cheng1, Zhaofeng Niu1, Zhouqiang Jiang2
1Computer Science Department, Qufu Normal University, Rizhao 276826, China.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
本研究介绍了增强的瓶概念学习器 (E-BotCL),这是一个自我监督的框架,用于可解释的深度学习. E-BotCL自主发现语义概念,在没有人类监督的情况下提高AI的透明度.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 深度神经网络 (DNN) 在图像分类方面表现出色,但缺乏透明度.
- 现有的可解释AI (XAI) 方法通常需要手动定义概念或缺乏语义对齐.
- 这限制了对医疗保健和自主系统等关键应用程序的信任和采用.
研究的目的:
- 引入增强的瓶概念学习器 (E-BotCL),这是一个新的自我监督框架.
- 允许在DNN中自主发现可解释的,与任务相关的语义概念.
- 在复杂的视觉任务中改善模型性能和透明度之间的平衡.
主要方法:
- E-BotCL 采用双路径对比式学习策略,用于强大的概念原型发现.
- 注意力机制用于学习概念的空间定位.
- 多任务规范化和功能聚合促进端到端的概念学习和分类.
主要成果:
- E-BotCL在可解释性指标方面显著改善,包括概念发现率 (CDR) 和概念一致性 (CC).
- 该框架在基准数据集的CDR (0.6104) 和CC (0.4486) 中取得了实质性的收益.
- 保持了分类准确性,同时提高了模型透明度.
结论:
- 在复杂的视觉任务中,E-BotCL为可解释的决策提供了一个可扩展的解决方案.
- 自主监督的方法消除了在概念学习中对人类监督的需求.
- 这项工作促进了可靠和透明的人工智能系统的发展.
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