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maml's Introduction

Model-Agnostic Meta-Learning

MAML is a model-agnostic optimization-based meta learning algorithm. It meta-trains a model to learn a parameter initialization such that it can be fine-tuned to a different task in a single gradient update.

This repository implements second-order MAML on the omniglot dataset

Requirements

  • PyTorch
  • OpenCV
  • Numpy
  • Tqdm

Usage

  1. Download the Omniglot Dataset's images_background.zip and images_evaluation.zip splits here.
  2. Unzip the files in omniglot/ directory.
  3. Run the train.py script to start the training with default options. Run python train.py -h to get a description of the arguments.
  4. For evaluation, run evaluate.py script.
  5. To make predictions on new data, refer Test.ipynb.
  6. Alternatively, Open In Colab

References

maml's People

Contributors

eceo902 avatar nerdimite avatar

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