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.
Source: Meta-Learning Requires Meta-AugmentationPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Meta-Learning | 3 | 60.00% |
Sequential Recommendation | 1 | 20.00% |
Domain Adaptation | 1 | 20.00% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |