Efficient Biologically Plausible Adversarial Training

This repository is the official implementation of the anonymous NeurIPS 2023 submission "Efficient Biologically Adversarial Training".

Details on the code organization

• adv_robustness_env.yaml (yaml): recommend environment for this repository (versions might change depending on your installations)
• configs (folder):
  • table_x (subfolder, where x can be 1, 2, 3, or 4): contains the yaml files with the different final tuned configs used for the results in Table x of the paper
• data (folder): contains the different datasets extracted after running a config (not uploaded)
• logs (folder): contains all logs from individual runs of final tuned configs provided the option `save_performances_values is True in the config
  • net_models (subfolder): contains the saved trained pytorch networks
  • results (subfolder): contains the testing and adversarial accuracy performances for every epoch
• utils (folder):
  • aux_functions.py (script): contains a progression bar function and the function to load the specified datasets
  • net_models.py (script): contains the class instantiating the network models
  • train_and_test_functions.py (script): contains training (per epoch) and testing related functions
• run_main_config.py (script): runs a specified tuned config
• main_config.py (script): contains the main used for the sweep called by the script run_main_config.py
• deploy_config.py (script): runs all the results reported in the paper by calling the script run_main_config.py for each config in configs/table_x

Install Python packages

All the needed Python libraries can be installed with conda by running:

$ conda env create -f adv_robustness_env.yml

Running the tuned configs

To run all the tuned configs for the several implemented methods, run:

$ python3 deploy_config.py

To run a specific tuned configs, run:

$ python3 run_main_config.py

and change the config_name with the different possible config names in the folder configs.

You can also changes the parameters of each config in the folder configs (e.g. to run for more epochs) or add your own.