vernen9/metadl

AAAI 2021 - Workshop 'Meta-Learning and Co-Hosted Competition' challenge repository

MetaDL Challenge : Few-shot learning series

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This repository contains the code associated to the Meta-Learning challenge organized by :

• Adrian El Baz (U. Paris-Saclay; UPSud, France)
• Isabelle Guyon (U. Paris-Saclay; UPSud/INRIA, France and ChaLearn, USA)
• Zhengying Liu (U. Paris-Saclay; UPSud, France)
• Jan N. van Rijn (Leiden University, Netherlands)
• Sebastien Treguer (U. Paris-Saclay; UPSud, France)
• Joaquin Vanschoren (Eindhoven University, the Netherlands)
• Lisheng Sun (UPSud, France)

CodaLab competition link

Outline

I - Overview

II - Installation

III - References

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I - Overview

This is the official repository of the Meta-Learning workshop co-hosted competition for AAAI 2021.

The competition focus on few-shot learning for image classification. This is an online competition, i.e. you need to provide your submission as raw Python code that will be ran on the CodaLab platform. The code is designed to be a module and to be flexible and allows participants to any type of meta-learning algorithms.

You can find more informations on the ChaLearn website.

II - Installation

Make sure you first clone the repository. Then you can directly jump to the Starting kit to get started.

We provide 2 different ways of installing the repository.

• Via a conda environment
• Via a Docker image

Follow the README.md in either case.

III - References

• [1] - E. Triantafillou Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples -- 2019
• [2] - J. Snell et al. Prototypical Networks for Few-shot Learning -- 2017
• [3] - C. Finn et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks -- 2017
• [4] - Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332-1338.

Disclamer

This module reuses some parts of the recent publication code E. Triantafillou et al. Meta-Dataset: GitHub repository regarging the data generation pipeline. Also the methods in the starting_kit/tutorial.ipynb such as plot_episode(), plot_batch(), iterate_dataset() have been taken from their introduction notebook.