Composing without forgetting

In this project, we propose a modular continual learning approach to face the problem of catastrophic forgettingand transfer in learning from evolving task distributions. Concretely, we propose a model that learns how to selectmost relevant modules based on a local decision rule for a given task to form a deep learning model for solving agiven task. In this framework we generalization to unseen but related tasks emerge through the composition ofthose modules.