Transferability estimation is an essential problem in transfer learning to predict how good the performance is when transferring a source model (or source task) to a target task. Recent analytical transferability metrics have been widely used for source model selection and multi-task learning. A major challenge is how to make transfereability estimation robust under the cross-domain cross-task settings. The recently proposed OTCE score solves this problem by considering both domain and task differences, with the help of transfer experiences on auxiliary tasks, which causes an efficiency overhead. In this work, we propose a practical transferability metric called JC-NCE score that dramatically improves the robustness of the task difference estimation in OTCE, thus removing the need for auxiliary tasks. Specifically, we build the joint correspondences between source and target data via solving an optimal transport problem with a ground cost considering both the sample distance and label distance, and then compute the transferability score as the negative conditional entropy of the matched labels. Extensive validations under the intra-dataset and inter-dataset transfer settings demonstrate that our JC-NCE score outperforms the auxiliary-task free version of OTCE for 7% and 12%, respectively, and is also more robust than other existing transferability metrics on average.
翻译:转让估计是转让学习以预测在将源模型(或源任务)转让到目标任务时业绩如何好的一个基本问题。最近的分析转让指标已被广泛用于源模型选择和多任务学习。一个重大挑战是如何使跨域跨任务设置下的可转让性估计变得稳健。最近提议的 OTCE 评分既考虑到领域差异,又考虑到任务差异,在辅助任务转让经验的帮助下,通过对产生效率间接费用的辅助任务转让经验,解决了这一问题。在这项工作中,我们提出了称为 JC-NCE 评分的实用可转让性指标,大大提高了 OTCE 任务差异估计的稳健性,从而消除了对辅助任务的需求。具体地说,我们通过解决最佳运输问题,在源和目标数据之间建立了源与目标数据之间的联合通信,考虑到抽样距离和标签距离,地面成本,然后将可转让性评分算作为匹配标签的负条件封号。在内部数据集和跨数据传输环境下进行的广泛验证,表明我们的JC-NCE的可转让性评分率大大超过对OCE任务估计的稳妥为平均百分比的其他版本。