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Learning to Transfer Learn: Reinforcement Learning-Based Selection for Adaptive Transfer Learning
[article]
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
arXiv:1908.11406v2
fatcat:ovmzwhgxzzckhl7jwyztskvjeq