Improving Few-Shot Learning with Auxiliary Self-Supervised Pretext Tasks

The paper is available on arxiv.

Installing

You need to have anaconda installed to run this repository. Miniconda does the job perfectly.

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

We provided a file containing all dependencies required to run any experiments. We recommend using it inside a new virtual environment.

conda env create -n improving-fs-ssl
conda env update -f environement.yml

Some development libraries may also be useful.

conda env update -f environment-dev.yml

Finally you have to install the current project.

pip install -e .

Experiments

Notebook

It is possible to run experiments with collab by using the notebook notebook/improving-fs-ssl.ipynb.

Training

Experiments are launched via ./scripts/train.py. To reproduce some experiments, e.g CIFAR-FS - Sup. + BYOL, you can specified a combination of multiple configuration files that are provided under the config directory.

./scripts/train.py -c config/cifar_fs.yml config/supervised_byol.yml config/seed_1.yml -o /path/to/trained/experiment/

It is also possible to create your own configuration file to override some defaults, all defined in mcp/config/parser.py.

Evaluation

Evaluation are simply ran using the script ./scripts/eval.py. You simply need to provide the path to a trained experiment and the evaluation configuration will be read automatically.

./scripts/eval.py -r /path/to/trained/experiment/

Visualize

To plot some default visualizations and get some results, you may run the script ./scripts/viz.py similar to evaluation. The plots will be saved under the path provided.

./scripts/viz.py -r /path/to/trained/experiment/