Learning to Transfer Learn: Reinforcement Learning-Based Selection for Adaptive Transfer Learning [article]

Linchao Zhu, Sercan O. Arik, Yi Yang, Tomas Pfister
2020 arXiv   pre-print
We propose a novel adaptive transfer learning framework, learning to transfer learn (L2TL), to improve performance on a target dataset by careful extraction of the related information from a source dataset. Our framework considers cooperative optimization of shared weights between models for source and target tasks, and adjusts the constituent loss weights adaptively. The adaptation of the weights is based on a reinforcement learning (RL) selection policy, guided with a performance metric on
more » ... target validation set. We demonstrate that L2TL outperforms fine-tuning baselines and other adaptive transfer learning methods on eight datasets. In the regimes of small-scale target datasets and significant label mismatch between source and target datasets, L2TL shows particularly large benefits.
arXiv:1908.11406v2 fatcat:ovmzwhgxzzckhl7jwyztskvjeq