We consider transferability estimation, the problem of estimating how well deep learning models transfer from a source to a target task. We focus on regression tasks, which received little previous attention, and propose two simple and computationally efficient approaches that estimate transferability based on the negative regularized mean squared error of a linear regression model. We prove novel theoretical results connecting our approaches to the actual transferability of the optimal target models obtained from the transfer learning process. Despite their simplicity, our approaches significantly outperform existing state-of-the-art regression transferability estimators in both accuracy and efficiency. On two large-scale keypoint regression benchmarks, our approaches yield 12% to 36% better results on average while being at least 27% faster than previous state-of-the-art methods.

翻译：我们考虑可迁移性估计问题，即评估深度学习模型从源任务迁移到目标任务时的表现。本文聚焦于此前鲜少被关注的回归任务，提出两种简单且计算高效的方法，通过线性回归模型的负正则化均方误差来估计可迁移性。我们证明了新理论结果，将我们的方法与迁移学习过程中获得的最优目标模型的实际可迁移性联系起来。尽管方法简单，但我们的方法在准确性和效率上均显著优于现有的最先进回归可迁移性估计器。在两个大规模关键点回归基准测试中，我们的方法平均性能提升12%至36%，同时速度比此前最先进方法快至少27%。