基于变压器的模型是否比基于CNN的模型更强大?
Zhendong Liu1, Shuwei Qian1, Changhong Xia1
1Nanjing University, 163 Xianlin Avenue, Qixia District, Nanjing, Jiangsu 210023, China.
概括
在现实世界的人工智能应用中,变压器模型与CNN相比显示出更高的稳定性. 本研究引入了新的指标和方法,以提高对数据腐败的深度学习模型的稳定性.
科学领域:
- 人工智能的人工智能
- 深度学习 (Deep Learning) 是一种深度学习.
- 计算机视觉 计算机视觉
背景情况:
- 越来越多的人工智能模型的部署需要在开放环境中提供强大的性能.
- 评估深度学习模型,特别是变压器和CNN的稳定性至关重要.
- 现有的稳定性指标可能无法完全捕捉现实世界的性能.
研究的目的:
- 为了比较基于变压器和基于CNN的深度学习模型的稳定性.
- 从结构和过程的角度确定稳健性的来源.
- 开发更好的评估指标和增强方法,以提高人工智能模型的稳定性.
主要方法:
- 在强度指标上对变压器和CNN模型进行比较分析.
- 通过富里埃变换和游戏相互作用分析对强度的研究.
- 开发一个校准的评估指标和基于模糊的增强方法.
主要成果:
- 变压器模型通常在各种指标上表现出比CNN模型更好的稳定性.
- 分析揭示了对变压器强度的潜在机制的见解.
- 拟议的校准度量和基于模糊的方法取得了最先进的结果.
结论:
- 变压器模型为人工智能部署提供了增强的稳定性.
- 新的评估和增强策略可以显著提高模型的弹性.
- 在基准数据集 (CIFAR-10-C,CIFAR-100-C,TinyImageNet-C) 上实现了最先进的性能.
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