maxtli/neuronshapley

Neuron Shapley: Discovering the Responsible Neurons

Xander Davies, Max Nadeau, Max Li

This repository is an implementation of the biased filter removal experiment in Ghorbani et al's Neuron Shapley: Discovering the Responsible Neurons (2020).

Organization:

• datasets.py configures the CelebA and FairFace datasets and dataloaders.
• model.py configures a SqueezeNet model for the binary gender detection class. It also defines a epoch function, which is a generic train/test loop.
• train_squeeze.py finetunes a ImageNet-pretrained SqueezeNet on a gender detection task from the CelebA dataset, and evaluates the resulting model on 500 images from each Black/White Male/Female split of FairFace.
• mab.py finds Shapley values for each filter on the FairFace dataset using the multi-armed bandit algorithm described by Ghorbani et al., and outputs approximations of filter shapley values to cur_records/shapley_values.pkl.
• eval.py evaluates the accuracy on FairFace (decomposed by race and gender) when removing filters with negative shapley values.
• squeezenet.pth stores the weights of a SqueezeNet after two epochs of fine tuning on the CelebA gender detection task.
• cur_records/shapley_values.pkl stores the shapley values obtained after 809 iterations of the MAB algorithm.

Necessary Datasets:

• ./data/celeba should contain the CelebA dataset, stored as *.jpg files within test, train, and val subfolders. The attributes files should be stored in list_landmarks_align_celeba.csv
• ./data/fairface should contain the FairFace dataset, with train and val subfolders and a fairface_label_val.csv label file.

Citation

@article{@misc{https://doi.org/10.48550/arxiv.2002.09815,
  doi = {10.48550/ARXIV.2002.09815},
  url = {https://arxiv.org/abs/2002.09815},
  author = {Ghorbani, Amirata and Zou, James},
  keywords = {Machine Learning (stat.ML), Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), Neural and Evolutionary Computing (cs.NE), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Neuron Shapley: Discovering the Responsible Neurons},
  publisher = {arXiv},
  year = {2020},
  copyright = {arXiv.org perpetual, non-exclusive license}
}