使用证据深度学习学习学习和预测未知的类
1The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo, 182-8585, Japan. naga0862@uec.ac.jp.
Scientific reports
|September 9, 2023
概括
修改证据深度学习 (m-EDL) 通过自然使用预测不确定性来识别未知的类来增强分类. 这种方法提高了已知类的准确性,即使存在未知的数据,也超过了标准的证据深度学习 (EDL).
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:
- 深度学习模型在医学成像和自动驾驶等关键应用中需要准确的不确定性估计.
- 证据深度学习 (EDL) 提供了不确定性,但与未知的类和用户定义的值作斗争.
研究的目的:
- 提出一种新的分类方法,即修改-证据深度学习 (m-EDL),该方法解决了EDL在处理未知类的局限性.
- 在存在不确定或未知的数据的情况下,提高深度学习分类模型的准确性和稳定性.
主要方法:
- 扩展经典EDL创建m-EDL,这自然输出预测不确定性作为一个未知的类的概率.
- 在三个不同的数据集上评估m-EDL,将其性能与标准EDL进行比较.
主要成果:
- 在存在未知类时,m-EDL在对已知类进行分类方面表现优于EDL.
- m-EDL在预测未知的类中表现出强度,即使它们的属性在训练和测试数据之间有所不同.
- 该研究发现,在培训期间混合未知的类数据需要类似于已知的类的特征来进行最佳的歧视.
结论:
- 与传统EDL相比,m-EDL提供了一种更自然,更准确的方法来处理未知的类.
- 拟议的方法将深度学习分类模型的适用性扩展到具有不可预测数据的真实场景.
- 通过有效量化和利用不确定性,m-EDL提高了深度学习预测的可靠性.
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