https://github.com/gitabcworld/FewShotLearning

Pytorch implementation of the paper "Optimization as a Model for Few-Shot Learning"
https://github.com/gitabcworld/FewShotLearning

few-shot optimization optimization-algorithms python pytorch

Last synced: 30 days ago
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Pytorch implementation of the paper "Optimization as a Model for Few-Shot Learning"

• Host: GitHub
• URL: https://github.com/gitabcworld/FewShotLearning
• Owner: gitabcworld
• License: mit
• Created: 2017-08-10T09:42:09.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2017-09-22T08:26:21.000Z (over 6 years ago)
• Last Synced: 2024-01-27T23:44:40.999Z (4 months ago)
• Topics: few-shot, optimization, optimization-algorithms, python, pytorch
• Language: Python
• Size: 32.2 KB
• Stars: 250
• Watchers: 8
• Forks: 57
• Open Issues: 9
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-stars - gitabcworld/FewShotLearning - Pytorch implementation of the paper "Optimization as a Model for Few-Shot Learning" (Python)
• Awesome-pytorch-list - FewShotLearning - Shot Learning" (Paper implementations / Other libraries:)
• Awesome-pytorch-list-CNVersion - FewShotLearning - Shot Learning" (Paper implementations｜论文实现 / Other libraries｜其他库:)

README

        
# Optimization as a Model for Few-Shot Learning

This repo provides a Pytorch implementation for the [Optimization as a Model for Few-Shot Learning](https://openreview.net/pdf?id=rJY0-Kcll) paper.

## Installation of pytorch

The experiments needs installing [Pytorch](http://pytorch.org/)

## Data 

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

```

pathImageNet = '/ILSVRC2012_img_train'

pathminiImageNet = '/miniImagenet/'

```

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

```

parser.add_argument('--dataroot', type=str, default='/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](https://scholar.google.es/citations?user=HJx2fRsAAAAJ&hl=en)