The aim for this repository is to contain clean, readable and tested code to reproduce few-shot learning research. See pytorch implements here.

This project is written in python3.6+ and paddle in AI Studio and assumes you have a GPU.

See these Medium articles for some more information

• Theory and concepts
• Discussion of implementation details

Strcuture

├── pretrained/
│   ├── omniglot_n=1_k=5_q=15_nv=1_kv=5_qv=1_dist=l2_fce=None.pdparams       
├── few_shot/                
│   ├── __init__
│   ├── callbacks
│   ├── core
│   ├── datasets
│   ├── eval
│   ├── maml
│   ├── matching
│   ├── metrics
│   ├── models
│   ├── proto
│   ├── train
│   ├── utils
├── config                 
├── matching_nets 
├── prepare_omniglot        
├── requirements.txt                      
├── README.md

Setup

Requirements

Listed in requirements.txt. Install with pip install -r requirements.txt preferably in a virtualenv.

Data

Edit the DATA_PATH variable in config.py to the location where you store the Omniglot and miniImagenet datasets.

After acquiring the data and running the setup scripts your folder structure should look like

DATA_PATH/
    Omniglot/
        images_background/
        images_evaluation/
    miniImageNet/
        images_background/
        images_evaluation/

Omniglot dataset. Download from https://github.com/brendenlake/omniglot/tree/master/python, place the extracted files into DATA_PATH/Omniglot_Raw and run prepare_omniglot.py

Train

This repo take omniglot datasets as default for training. You can run as below ( parameter description refer to Arguments ):

python -m matching_nets.py --dataset omniglot --fce False --k-test 5 --n-test 1 --distance l2

Matching Networks

Arguments

• dataset: {'omniglot', 'miniImageNet'}. Whether to use the Omniglot or miniImagenet dataset
• distance: {'l2', 'cosine'}. Which distance metric to use
• n-train: Support samples per class for training tasks
• n-test: Support samples per class for validation tasks
• k-train: Number of classes in training tasks
• k-test: Number of classes in validation tasks
• q-train: Query samples per class for training tasks
• q-test: Query samples per class for validation tasks
• fce: Whether (True) or not (False) to use full context embeddings (FCE)
• lstm-layers: Number of LSTM layers to use in the support set FCE
• unrolling-steps: Number of unrolling steps to use when calculating FCE of the query sample

I had trouble reproducing the results of this paper using the cosine distance metric as I found the converge to be slow and final performance dependent on the random initialisation. However I was able to reproduce (and slightly exceed) the results of this paper using the l2 distance metric.

	Omniglot
k-way	5
n-shot	1
Pytorch Published (l2)	98.3
This paddle Repo (l2)	98.85

Final

• See pretrained model in pretrained file.