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https://github.com/nathanielsimard/improving-fs-ssl
Improving Few-Shot Learning with Auxiliary Self-Supervised Pretext Tasks
https://github.com/nathanielsimard/improving-fs-ssl
few-shot-learning representation-learning self-supervised-learning
Last synced: 16 days ago
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Improving Few-Shot Learning with Auxiliary Self-Supervised Pretext Tasks
- Host: GitHub
- URL: https://github.com/nathanielsimard/improving-fs-ssl
- Owner: nathanielsimard
- License: mit
- Created: 2020-09-22T19:50:49.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2021-02-24T18:25:58.000Z (over 3 years ago)
- Last Synced: 2024-10-04T16:53:05.409Z (about 1 month ago)
- Topics: few-shot-learning, representation-learning, self-supervised-learning
- Language: Python
- Homepage: https://arxiv.org/abs/2101.09825
- Size: 125 KB
- Stars: 13
- Watchers: 4
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Improving Few-Shot Learning with Auxiliary Self-Supervised Pretext Tasks
The paper is available on [arxiv](https://arxiv.org/abs/2101.09825).
## Installing
You need to have anaconda installed to run this repository.
Miniconda does the job perfectly.
```bash
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.
```bash
conda env create -n improving-fs-ssl
conda env update -f environement.yml
```
Some development libraries may also be useful.
```bash
conda env update -f environment-dev.yml
```
Finally you have to install the current project.
```bash
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.
```bash
./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.
```bash
./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.
```bash
./scripts/viz.py -r /path/to/trained/experiment/
```