Transfer learning can significantly improve the sample efficiency of neural networks, by exploiting relatedness between a data-scarce target task and data-abundant source task. Despite years successful applications, transfer practice often relies on ad-hoc solutions, while theoretical understanding these procedures is still limited. In present work, we re-think solvable model synthetic data as ...
