Overview

This work contains the PyTorch implementation of and demonstrations of NeurIPS 2021: Self-Interpretable Model with Transformation Equivariant Interpretation (SITE)

Method

SITE trains a self-interpretable model that offers both consistent predictions and explanations across geometric transformations. This is achieved through the regularization of a self-interpretable module, thereby increasing the model's trustworthiness.

For academic usage, please consider citing:

  @article{wang2021self,
    title={Self-interpretable model with transformation equivariant interpretation},
    author={Wang, Yipei and Wang, Xiaoqian},
    journal={Advances in Neural Information Processing Systems},
    volume={34},
    pages={2359--2372},
    year={2021}
  }

Contents

Libraries

  numpy==1.19.5
  torch==1.10.2
  torchvision=0.11.3

Training notebooks demonstrate the training process of SITE on MNIST and CIFAR datasets.

Example notebooks demonstrate how SITE is used to generate explanations for MNIST and CIFAR datasets