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在生成模型中等级通货膨胀

Phuc Nguyen1, Miao Li1, Alexandra Morgan1

  • 1Department of Pathology at Beth Israel Deaconess Medical Center (BIDMC), Boston, MA 02215.

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概括
此摘要是机器生成的。

常见的生成模型评估得分膨胀性能,误导研究人员. 像伊甸园得分一样,一个新的类型的"均等性"得分,避免了这种"等级通货膨胀",更好地匹配人类的判断.

关键词:
生成型模型是一种生成型模型.丘陵地区的多样性杰卡德的得分得分.库尔巴克 - 莱布勒分歧没有任何的.相关性得分的相关性得分.地球移动器的距离消极的顺序是负的.负视角参数 负视角参数质量评分 质量评分 质量评分综合数据 综合数据表格式数据是表格式数据.

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科学领域:

  • 机器学习 机器学习
  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 生成模型需要可靠的评估指标来评估合成数据的质量.
  • 用于比较分布的现有分数往往提供过于乐观的绩效评估.

研究的目的:

  • 在常用的生成模型评估得分中识别和解释"等级通货膨胀问题".
  • 引入一类新的"均等性"评分,旨在克服这一局限性.
  • 将伊甸园得分作为一种新的等效得分,并评估其表现.

主要方法:

  • 分析广泛使用的分数:相关性,雅卡德,地球移动器和库尔巴克-莱布勒 (相对).
  • 引入了"等分"得分概念,其中所有数据点的价值均等.
  • 伊甸分数的开发和测试,这是一个"平衡性"分数的例子.

主要成果:

  • 通常使用的"平衡点"得分 (相关性,贾卡德,地球移动器,KL分歧) 显示出"等级通货膨胀".
  • 提出的伊甸分数,一个"平衡性"分数,成功地避免了年级通货膨胀.
  • 伊甸园得分显示了与人类对数据分布匹配的感知有所改善的一致性,与等点得分相比.

结论:

  • 均衡分数在评估生成模型时本质上容易受到等级通货膨胀的影响.
  • 以伊甸为例的等同度得分,为评估生成模型提供了一种更强大,更符合感知的方法.
  • 建议用于比较低维分布的均等性得分,特别是在生成AI的背景下.