通过在输出层旁边执行聚合决策来提高准确性
Yuval Meir1, Yarden Tzach1, Ronit D Gross1
1Department of Physics, Bar-Ilan University, 52900, Ramat Gan, Israel.
Scientific reports
|August 31, 2023
概括
在输出层附近的战略聚合提高了深度学习分类准确性. 这种方法训练有影响力的路线,优于标准的最大集成策略,以提高图像识别性能.
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:
- 深度学习模型通常在整个前架构中使用最大聚合 (MP) 运算符来执行分类任务.
- 聚合运营商的位置和类型显著影响模型性能和功能选择.
研究的目的:
- 调查聚合操作员位置,特别是与最后一个卷积层相邻的位置,对分类准确性的影响.
- 为了比较高级VGG (A-VGG) 架构与修改的聚合策略的性能,与标准深度学习模型进行比较.
主要方法:
- 利用CIFAR-10数据库来评估深度学习模型的性能.
- 实施并测试了各种高级VGG (A-VGGm) 架构,在输出层附近修改了聚合策略.
- 与VGG16和Wide-ResNet16等既有架构进行了比较.
主要成果:
- 将与最后一个卷积层相邻的决策汇集起来,显著提高了分类准确性.
- A-VGG8实现了比VGG16更高的精度,而A-VGG13/A-VGG16与Wide-ResNet16的性能相匹配.
- 对完全连接 (FC) 层的修改也产生了可比的准确性,表明了输出相邻的聚合的主要影响.
结论:
- 聚合运营商的战略位置,特别是平均聚合,与输出层相邻,对于改善深度学习分类至关重要.
- 这种方法有效地训练出更有影响力的输入-输出路线,与传统的深度架构相比,带来更高的性能.
- 结果建议重新评估现有的深度学习架构及其报告的准确性,使用此拟议的聚合策略.
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