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MAML-Pytorch is a PyTorch implementation of Model-Agnostic Meta-Learning for supervised learning experiments. It focuses on reproducing and exploring the MAML approach for few-shot learning research. The repository supports MiniImagenet and Omniglot, two common benchmark datasets for meta-learning experiments. It includes separate training scripts, dataset loaders, learner components, and meta-learning logic. The project also notes that MAML can be difficult to train and presents the implementation as a practical starting point for research. Overall, it is useful for students and researchers who want to study fast adaptation, few-shot classification, and gradient-based meta-learning in PyTorch.
Features
- PyTorch implementation of Model-Agnostic Meta-Learning
- Support for MiniImagenet and Omniglot experiments
- Few-shot supervised learning research focus
- Meta learner and basic learner implementation
- Training scripts for benchmark datasets
- MIT licensed research starting point
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