Project realized for the "Deep leraning" course (ENSAE last year, DSSA track).
We aim at applying the method proposed in the paper "Score-based generative modeling through stochastic differential equation" on another data set so as to generate images.
You can run these notebooks on Colab :
| Notebook-dataset | path | Colab Link |
|---|---|---|
| letter-emnist (letter emnist matfile required) | ./notebooks/letter-mnist/training_sampling_notebook_lettermnist.ipynb | Colab Link |
| icon (icon matfile required) | ./notebooks/icon/training_sampling_notebook_icon.ipynb | Colab Link |
| notmnist | ./notebooks/notmnist/training_sampling_notebook_notmnist.ipynb | Colab Link |
| kmnist | ./notebooks/kmnist/training_sampling_notebook_kmnist.ipynb | Colab Link |
| HASYv2 | ./notebooks/HASYv2/training_sampling_notebook_HASYv2.ipynb | Colab Link |
- The paper: https://arxiv.org/abs/2011.13456
- A blogpost explaining the paper: https://yang-song.net/blog/2021/score/
- Yang Song's tutorial implementing the paper (python, torch), on which our implementation is largely based : https://colab.research.google.com/drive/120kYYBOVa1i0TD85RjlEkFjaWDxSFUx3?usp=sharing
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Project based on the cookiecutter data science project template. #cookiecutterdatascience