This repository contains accompanying code for the article introducing Meta-Dataset, https://arxiv.org/abs/1903.03096.

This code is provided here in order to give more details on the implementation of the data-providing pipeline, our back-bones and models, as well as the experimental setting.

See below for user instructions, including how to install the software, download and convert the data, and train implemented models.

We are currently working on updating the code and improving the instructions to facilitate designing and running new experiments.

This is not an officially supported Google product.

Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle

Few-shot classification refers to learning a classifier for new classes given only a few examples. While a plethora of models have emerged to tackle this recently, we find the current procedure and datasets that are used to systematically assess progress in this setting lacking. To address this, we propose Meta-Dataset: a new benchmark for training and evaluating few-shot classifiers that is large-scale, consists of multiple datasets, and presents more natural and realistic tasks. The aim is to measure the ability of state-of-the-art models to leverage diverse sources of data to achieve higher generalization, and to evaluate that generalization ability in a more challenging and realistic setting. We additionally measure robustness to variations in the number of available examples and the number of classes. Finally our extensive empirical evaluation leads us to identify weaknesses in Prototypical Networks and MAML, two popular few-shot classification methods, and to propose a new method, Proto-MAML, which achieves improved performance on our benchmark.

Meta-Dataset currently supports Python 2 only, and has not been tested with TensorFlow 2 yet.

• We recommend you follow these instructions to install TensorFlow.
• A list of packages to install is available in requirements.txt, you can install them using pip.
• Clone the meta-dataset repository. Most command lines start with python -m meta_dataset.<something>, and should be typed from within that clone (where a meta_dataset Python module should be visible).

Meta-Dataset uses several established datasets, that are available from different sources. You can find below a summary of these datasets, as well as instructions to download them and convert them into a common format.

For brevity of the command line examples, we assume the following environment variables are defined:

• $DATASRC: root of where the original data is downloaded and potentially extracted from compressed files. This directory does not need to be available after the data conversion is done.
• $SPLITS: directory where *_splits.pkl files will be created, one per dataset. For instance, $SPLITS/fungi_splits.pkl contains information about which classes are part of the meta-training, meta-validation, and meta-test set. This is only used during the dataset conversion phase, but can help troubleshooting later.
• $RECORDS: root directory that will contain the converted datasets (one per sub-directory). This directory needs to be available during training and evaluation.

Experiments are defined via gin configuration files, that are under meta_dataset/learn/gin/:

• setups/ contain generic setups for classes of experiment, for instance which datasets to use (imagenet or all), parameters for sampling the number of ways and shots of episodes.
• models/ define settings for different meta-learning algorithms (baselines, prototypical networks, MAML...)
• default/ contains files that each correspond to one experiment, mostly defining a setup and a model, with default values for training hyperparameters.
• best/ contains files with values for training hyperparameters that achieved the best performance during hyperparameter search.