gitabcworld / Fewshotlearning
Licence: mit
Pytorch implementation of the paper "Optimization as a Model for Few-Shot Learning"
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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
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