Meta-augmentation

2000년 도입 · 논문 4편에서 사용

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.

출처: Meta-Learning Requires Meta-Augmentation

소개 논문: Meta-Learning Requires Meta-Augmentation