超级KAN:科尔莫戈罗夫-阿诺德网络使得高光谱图像分类器变得更聪明
Nikita Firsov1, Evgeny Myasnikov1, Valeriy Lobanov1,2
1Samara National Research University, Samara 443086, Russia.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
科尔摩戈罗夫-阿诺德网络 (KANs) 通过取代传统的多层感知子 (MLPs) 来提高高光谱图像分类的准确性. 基于KAN的变压器在多个数据集中取得了最佳结果.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 遥感 遥感 遥感 遥感
背景情况:
- 多层感知子 (MLP) 是神经网络分类中的标准.
- 科尔摩戈罗夫-阿诺德网络 (KANs) 提供了一种新的替代方案,具有提高准确性的潜力.
- 对超光谱图像的像素分类对于遥感应用至关重要.
研究的目的:
- 调查基于KAN的网络对高光谱图像分类的有效性.
- 将KAN性能与传统的MLP进行比较.
- 评估现有架构中的线性,卷积和注意层的KAN替代方案.
主要方法:
- 基线MLP和KAN网络与各种隐藏层神经元数量的比较分析.
- 通过整合KAN层来修改六个最先进的神经网络 (1DCNN,2DCNN,2个3DCNN,NM3DCNN,SSFTT).
- 使用七个多样化,公开可用的高光谱数据集进行实验验证.
主要成果:
- 基于KAN的网络在所有测试的架构中始终优于基线MLP网络.
- 在所有修改后的网络中,观察到像素分类精度的显著改善.
- 基于KAN的变压器架构 (SSFTT) 产生了最高的分类准确性.
结论:
- 在高光谱图像分类任务中,KAN代表了MLP的优越替代方案.
- 用KAN对应物取代传统层可以提高各种神经网络架构的性能.
- 基于KAN的变压器在超光谱图像分析中实现最先进的结果方面表现出非常有前途.
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