对图像分类任务中的简单性偏差的现代视角
Xiaoguang Chang1, Teng Wang2, Changyin Sun2
1School of Cyber Science and Engineering, Southeast University, Nanjing, China.
概括
神经网络中的简单性偏差 (SB) 有助于概括. 本研究介绍了CLIP模型中SB的频率感知度,揭示了它对各种图像分类任务的影响,并指导了最佳模型设计.
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:
- 简单性偏差 (SB) 对神经网络概括至关重要,但其对现实世界的影响尚不清楚.
- 现有的复杂度指标可能不准确地评估SB,将其与模型表达性混为一谈.
研究的目的:
- 系统地研究CLIP模型中的SB及其对图像分类概括的影响.
- 提出一种新的频率意识测量方法来量化SB.
- 评估SB调制在各种泛化任务中的权衡.
主要方法:
- 复杂度指标的理论分析.
- 在频率受限制的输入空间中使用1D插入的频率感知SB测量的开发.
- 在CLIP模型中SB的调制,用于对OOD,图像腐败和对抗任务的零射击和微调评估.
主要成果:
- 拟议的措施显示,SB将模型的灵敏度转移到低频组件上,解释了概括的改进.
- 更强大的SB增强了分布之外 (OOD) 的普遍化.
- 更强大的SB与敌对强度的降低相关.
结论:
- 在CLIP模型的概括能力方面,SB发挥着重要作用.
- 一个频率意识测量为SB机制提供了更深入的见解.
- 平衡SB对于优化不同泛化场景中的模型性能至关重要,特别是OOD与对抗性稳定性.
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