EKDSC:基于专家知识蒸的长尾认可,针对特定类别的专家知识蒸
Yaping Bai1, Jinghua Li1, Dehui Kong1
1Beijing Key Laboratory of Multimedia and Intelligent Software Technology, School of Information Science and Technology, Beijing University of Technology, Beijing, 100124, China; Beijing Institute of Artificial Intelligence, School of Information Science and Technology, Beijing University of Technology, Beijing, 100124, China.
概括
专家知识蒸特定类别 (EKDSC) 通过培训专业教师模型来提高长尾视觉识别. 这种方法提高了尾部等级的准确性,同时保持了头部等级的性能,超过了当前最先进的方法.
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:
- 由于数据分布不平衡,长尾视觉识别在头尾类之间存在性能差异.
- 现有的方法往往会提高尾部级的性能,而牺牲头部级的准确性.
- 有效地转移外部知识以解决这种不平衡仍然是一个挑战.
研究的目的:
- 提出一种新的方法,专家知识蒸特定类别 (EKDSC),以解决长尾视觉识别的性能差距.
- 为了提高尾部类的识别精度,同时减轻头部类的性能退化.
- 探索专业知识从多专家教师模型到学生模型的有效转移.
主要方法:
- 开发了一个专门的教师模式,为头部,中部和尾部班级提供不同的专家,以确保集中学习.
- 实施了知识蒸策略,每个专家教师模型将其专业知识转移到学生模型中.
- 在各种基准数据集上评估EKDSC方法,包括CIFAR-10 LT,CIFAR-100 LT,ImageNet-LT,iNaturalist 2018和Places-LT.
主要成果:
- 在长尾视觉识别任务中,EKDSC显著提高了尾部类的准确性.
- 提出的方法有效地减轻了在头类中观察到的常见性能下降.
- 取得了最先进的 (SOTA) 结果,在多个基准数据集上表现比现有方法优于1-5%.
结论:
- 通过在所有类别中平衡性能,EKDSC为长尾视觉识别问题提供了强大的解决方案.
- 基于专家的知识蒸方法在转移专业知识以改善识别方面是有效的.
- 该方法在不同规模的数据集中展示了强大的概括能力.
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