Meta-Learning without Data via Wasserstein Distributionally-Robust Model Fusion

Existing meta-learning works assume that each task has available training and testing data. However, we can only use many available pre-trained models without accessing their training data in practice. We often need a single model to solve different tasks simultaneously as this is much more convenient to deploy the models. Our work aims to meta-learn a model initialization from these pre-trained models without using corresponding training data. We name this challenging problem setting Data-Free Learning To Learn (DFL2L). We propose a distributionally robust optimization (DRO) framework to learn a black-box model to fuse and compress all the pre-trained models into a single network to address this problem. The proposed DRO framework diversifies the learned task embedding associated with each pre-trained model to cover the diversity in the underlying training task distributions, encouraging good generalization to unseen new tasks. We sample a meta-initialization from the black-box network during meta-testing for fast adaptation to unseen new tasks. Extensive experiments on offline and online DFL2L settings and several real image datasets demonstrate the effectiveness of the proposed methods.