Felix Grezes, Finite Gaussian Neurons for Adversarial Defense, 2021, https://github.com/grezesf/FGN---Research.
@misc{grezes2021,
author={Felix Grezes},
year={2021},
title={Finite Gaussian Neurons for Adversarial Defense},
howpublished={\url{https://github.com/grezesf/FGN---Research}},
}
PyTorch
Torchvisionn
Numpy
Scipy
Matplotlib
----\
|
|---\Finite_Gaussian_Network_lib
# functional library to run FGNs
|
|---\fgn_helper_lib
# useful functions not stricly related to FGNs
|
|---\tests
# tests for the library functions
|
|---\Notebooks
# notebooks to plot results, visualize data, etc...
|
|---\Experiments
# contains scripts to run experiments and the results
|
|---\dev
# development work
|
|---\old
# old work
A collection of functions related to Finite Gaussian Networks.
- Matlab style: one function per file. Open a file to see it's definition, parameters, etc...
- the fgn_helper_lib directory contains randoms useful functions, but that don't directly relate to FGNs.
- the tests directory contains tests for functions in the library
A collection of Jupyter Notebooks used for data visualization, results plotting, experiments analysis, etc... Loosely follows the narrative of the thesis.
A collection of tiny scripts that run experiments, and folders containing the results.
The scripts should be tiny, only creating the folders, setting the parameters and calling the library function.
Convention: scripts should create timestamped folders for the results each run.
mnist_fgn_train.py
should create `/res-mnist_fgn_train-time:stamp'
collection of notebooks used to develop the FGN library functions, the scripts.