论文标题
PACTRAN：PAC-Bayesian指标，用于估计预算模型对分类任务的可传递性
PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification Tasks
论文作者
论文摘要
近年来，随着预读模型的越来越多，为特定的下游分类任务选择最佳的检查站的问题一直在增加注意力。尽管最近提出了几种方法来解决选择问题（例如LEEP，H-SCORE），但这些方法诉诸应用学习理论并非充分动机的启发式方法。在本文中，我们介绍了PACTRAN，这是一个理论上扎根的指标家族，用于验证的模型选择和可传递性测量。我们首先展示了如何从转移学习设置下的最佳PAC-Bayesian界来得出PACTRAN指标。然后，我们在许多视觉任务（VTAB）以及语言和视觉（OKVQA）任务上对PACTRAN的三个度量实例进行了经验评估。对结果的分析表明，与现有选择方法相比，PACTRAN是一种更一致，更有效的可传递性度量。
With the increasing abundance of pretrained models in recent years, the problem of selecting the best pretrained checkpoint for a particular downstream classification task has been gaining increased attention. Although several methods have recently been proposed to tackle the selection problem (e.g. LEEP, H-score), these methods resort to applying heuristics that are not well motivated by learning theory. In this paper we present PACTran, a theoretically grounded family of metrics for pretrained model selection and transferability measurement. We first show how to derive PACTran metrics from the optimal PAC-Bayesian bound under the transfer learning setting. We then empirically evaluate three metric instantiations of PACTran on a number of vision tasks (VTAB) as well as a language-and-vision (OKVQA) task. An analysis of the results shows PACTran is a more consistent and effective transferability measure compared to existing selection methods.
