Repository for the paper Learning curves of generic features maps for realistic datasets with a teacher-student model.
We provide a couple of guided examples to help the reader reproduce the figures of the paper. The key ingredients are:
| File | Description |
|---|---|
/state_evolution/ |
Out-of-the-box package for solving saddle-points equations for classification and regression tasks, |
how_to.ipynb |
Notebook with a step-by-step explanation on how to use the state_evolution package. |
/real_data/mnist_scattering.ipynb |
Notebook reproducing real-data curves, see Fig. 4 of the paper. |
/gan_data/synthetic_data_pipeline.ipynb |
Notebook explaining pipeline to assign labels for GAN generated data. |
/gan_data/monte_carlo.ipynb |
Notebook explaining how to estimate population covariances for features from GAN generated data. |
/gan_data/learning_curves.ipynb |
Notebook reproducing learning curves for GAN generated data, see Fig. 3 of the paper. |
The notebooks are self-explanatory. You will also find some auxiliary files such as simulations.py in /real_data wrapping the code for running the simulations, and dcgan.py, teachers.py, teacherutils.py, simulate_gan.py in /gan_data/ wrapping the different torch models for the pre-trained generators and teachers.
Note that for running the examples in /gan_data you will need the weights of the generator, the teacher and the covariances. A folder can be downloaded here in a single folder /data.
[1]: Learning curves of generic features maps for realistic datasets with a teacher-student model, B Loureiro, C Gerbelot, H Cui, S Goldt, F Krzakala, M Mézard, L Zdeborová, arXiv: 2102.08127 [stat.ML]