A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch

A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch. Torchmeta contains popular meta-learning benchmarks, fully compatible with both torchvision and PyTorch’s DataLoader.

Features

• A unified interface for both few-shot classification and regression problems, to allow easy benchmarking on multiple problems and reproducibility.
• Helper functions for some popular problems, with default arguments from the literature.
• An thin extension of PyTorch’s Module, called MetaModule, that simplifies the creation of certain meta-learning models (e.g. gradient based meta-learning methods). See the MAML example for an example using MetaModule.

Datasets available

• Few-shot regression (toy problems):
• Few-shot classification (image classification):
  • Omniglot (Lake et al., 2015, 2019)
  • Mini-ImageNet (Vinyals et al., 2016, Ravi et al., 2017)
  • Tiered-ImageNet (Ren et al., 2018)
  • CIFAR-FS

     

     

     

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