This repository is for the paper, "Interpretable ML for Imbalanced Data." It contains the code and links to obtain pre-trained models, as well as the steps, to reproduce several of the visualizations listed in the paper. Please note that the code provided below is for the CIFAR-10 dataset.
- Extract FE from a trained model.
- Start with extract_FE.py
- link to pre-trained model used by Extract_FE: https://drive.google.com/file/d/18yWsVlUNVrSMq4qTlY2d9MPHThZaSWUK/view?usp=sharing
- Display class accuracy.
- class_accuracy.py
- Visualize class archetypes (safe, border, rare, outliers) for a specific class
- First, generate nearest neighbors with arch_NNB.py
- Alternatively, use the CE_cif_trn_NNB.csv file in the data folder.
- Run k_medoid_viz.py
- Visualize nearest adversary neighbors bar chart
- Run NNB_FP_bar.py
- Display feature embedding (FE) top-10 indices and FE densities
- Run FE_idx_density.py
- Visualize color bands for a specific class of interest
- Run saliency_texture.py