Pytorch implementation of the paper "Optimization as a Model for Few-Shot Learning"

Overview

Optimization as a Model for Few-Shot Learning

This repo provides a Pytorch implementation for the Optimization as a Model for Few-Shot Learning paper.

Installation of pytorch

The experiments needs installing Pytorch

Data

For the miniImageNet you need to download the ImageNet dataset and execute the script utils.create_miniImagenet.py changing the lines:

pathImageNet = '<path_to_downloaded_ImageNet>/ILSVRC2012_img_train'
pathminiImageNet = '<path_to_save_MiniImageNet>/miniImagenet/'

And also change the main file option.py line or pass it by command line arguments:

parser.add_argument('--dataroot', type=str, default='<path_to_save_MiniImageNet>/miniImagenet/',help='path to dataset')

Installation

$ pip install -r requirements.txt
$ python main.py 

Acknowledgements

Special thanks to @sachinravi14 for their Torch implementation. I intend to replicate their code using Pytorch. More details at https://github.com/twitter/meta-learning-lstm

Cite

@inproceedings{Sachin2017,
  title={Optimization as a model for few-shot learning},
  author={Ravi, Sachin and Larochelle, Hugo},
  booktitle={In International Conference on Learning Representations (ICLR)},
  year={2017}
}

Authors

  • Albert Berenguel (@aberenguel) Webpage
Owner
Albert Berenguel Centeno
Phd student and computer vision enthusiast. "There are no secrets to success. It is the result of preparation, hard work, and learning from failure" ColinPowell
Albert Berenguel Centeno
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