Task transfer learning is a popular technique in image processing applications that uses pre-trained models to reduce the supervision cost of related tasks. An important question is to determine task transferability, i.e. given a common input domain, estimating to what extent representations learned from a source task can help in learning a target task. Typically, transferability is either measured experimentally or inferred through task relatedness, which is often defined without a clear operational meaning. In this paper, we present a novel metric, H-score, an easily-computable evaluation function that estimates the performance of transferred representations from one task to another in classification problems using statistical and information theoretic principles. Experiments on real image data show that our metric is not only consistent with the empirical transferability measurement, but also useful to practitioners in applications such as source model selection and task transfer curriculum learning.
翻译:任务转移学习是图像处理应用中的一种流行技术,它使用预先培训的模型来降低相关任务的监督成本,一个重要问题是确定任务的可转移性,即考虑到一个共同的投入领域,估计从源任务中了解到的表述在多大程度上有助于学习目标任务。一般而言,可转移性是通过实验性衡量的,或是通过任务关联性推断的,而任务关联性的定义往往没有明确的操作意义。在本文件中,我们提出了一个新的衡量标准,即H-核心,一种容易计算的评价功能,它利用统计和信息理论原则来估计在分类问题中从一个任务转移到另一个任务的表现。对真实图像数据的实验表明,我们的衡量标准不仅符合经验转移性衡量标准,而且对诸如来源模式选择和任务转移课程学习等应用的从业人员也有用。