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What is: Meta-augmentation?

SourceMeta-Learning Requires Meta-Augmentation
Year2000
Data SourceCC BY-SA - https://paperswithcode.com

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.