深度分类与线性增强的逻辑到软max函数.
Hao Shao1, Shunfang Wang2,3
1School of Mathematics and Statistics, Yunnan Unverisity, Kunming 650504, China.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
对于卷积神经网络 (CNN) 来说,Orthogonal-Softmax是一种新的损失函数,通过提高特征可区分性来增强深度分类. 这种方法促进了类内紧性和类间差异,以更好地识别图像.
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:
- 包括图像识别和目标检测在内的深度分类任务正在迅速推进.
- 卷积神经网络 (CNN) 是这些进步的核心,软max是性能的关键组成部分.
- 现有的软max方法在特征区分和类别分离方面可能存在局限性.
研究的目的:
- 引入Orthogonal-Softmax,一个新的,直观的学习目标功能,用于深度分类任务.
- 为了增强由CNNs提取的特征的歧视力.
- 为了同时提高类内紧性和类间差异性.
主要方法:
- 开发了Orthogonal-Softmax,这是一个基于Gram-Schmidt正交的新型损失函数,用于线性近似.
- 使用直角多项式扩展,与传统和泰勒-软max相比,建立更强的关系.
- 设计一个线性软max损失以优化类别分离和特征紧性.
主要成果:
- 与传统和泰勒-软max相比,通过直角多项式扩展证明了更强的关系.
- 拟议的损失函数实际上获得了对分类具有高度歧视性的特征.
- 在四个基准数据集上的实验验验证了提出的方法在促进类内紧性和类间差异方面的有效性.
结论:
- 正角-软max提供了一个有前途的方法来提高深度分类性能在CNNs.
- 该方法成功地提高了特征的可区分性,从而导致更好的分类结果.
- 未来的工作可能会探索Orthogonal-Softmax对非地面真实样本的应用.
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