仔细看一下基准测试 自主监督的预训与图像分类
Markus Marks1, Manuel Knott1,2,3,4, Neehar Kondapaneni1
1California Institute of Technology, Pasadena, CA USA.
概括
自主监督学习 (SSL) 模型从未标记的数据中学习. 这项研究表明,线性/kNN探测协议最好预测SSL模型在新任务上的性能,无论数据集或架构如何. 批量规范化的批量规范化
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 自主监督学习 (SSL) 利用未标记的数据进行模型培训,减少对昂贵的手动标签的依赖.
- 在计算机视觉中,SSL对于预训练模型至关重要,它可以实现转移学习和少数镜头学习等任务.
- 评估SSL模型在各种下游任务中学习的表示的质量仍然是一个挑战.
研究的目的:
- 调查SSL基于分类的评估协议之间的相关性.
- 评估这些协议在各种数据集类型的下游性能预测有多好.
- 了解模型架构和数据集领域转移对评估协议可靠性的影响.
主要方法:
- 通过使用各种SSL方法,对11个图像数据集和26个预训练模型进行了全面的研究.
- 使用域内协议评估的SSL方法:微调,线性探测和k-最近邻居 (kNN).
- 分析了批量规范化和数据集域转移对协议性能和相关性的影响.
主要成果:
- 域内线性探测和kNN协议显示出对域外性能最强的平均预测能力.
- 发现歧视性和生成性SSL方法之间的大多数性能差异都归因于模型骨干变化.
- 确定评估协议的稳定性随着数据集领域的转变而变化.
结论:
- 线性/kNN探测可以作为有效的一般预测器,用于评估不同下游应用中的SSL表示质量.
- 模型骨干架构显著影响性能,通常比特定的SSL培训方法更大.
- 需要进一步的研究来完善SSL评估协议,以适应各种现实场景.
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