Meta-augmentation
Meta-augmentation
**Meta-augmentation** helps generate more varied tasks for a single example in meta-learning. It can be distinguished from data augmentation in classic machine learning as follows. For data augmentation in classical machine learning, the aim is to generate more varied examples, within a single task. Meta-augmentation has the exact opposite aim: we wish to generate more varied tasks,
for a single example, to force the learner to quickly learn a new task from feedback. In meta-augmentation, adding randomness discourages the base learner and model from learning trivial solutions that do not generalize to new tasks.
Meta-Learning Requires Meta-Augmentation
https://paperswithcode.com/paper/meta-learning-requires-meta-augmentation
Meta-Learning Requires Meta-Augmentation