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Regulating Model Reliance on Non-Robust Features by Smoothing Input Marginal Density (ECCV 2024)

This code implements an input marginal density regulariation from the following paper:

Peiyu Yang, Naveed Akhtar, Mubarak Shah, and Ajmal Mian

Regulating Model Reliance on Non-Robust Features by Smoothing Input Marginal Density

Introduction

Trustworthy machine learning necessitates meticulous regulation of model reliance on non-robust features. We propose a framework to regulate such features by attributing model predictions to the input. Within our approach, we identify a correlation between model reliance on non-robust features and smoothness of marginal density of the input samples. Hence, we uniquely regularize the gradients of the marginal density w.r.t.~the input features for robustness. We also devise an efficient implementation of our regularization to address the potential numerical instability of the underlying optimization process.

Prerequisites

• python 3.9.2
• matplotlib 3.5.1
• numpy 1.21.5
• pytorch 1.12.0
• torchvision 0.13.1
• tqdm 4.64.0

Re-calibrating attributions

Step 1: Preparing dataset.

dataset\DATASET

Step 2: Preparing models.

pretrained_models\YOUR_MODEL

Step 3: Re-calibrating attributions (IG Uniform).

python main.py

Quantitatively evaluations

python main.py 

Bibtex

If you found this work helpful for your research, please cite the following papers:

@artical{yang2024regulating,
    title={Regulating Model Reliance on Non-Robust Features by Smoothing Input Marginal Density},
    author={Peiyu, Yang and Naveed, Akhtar and Mubarak, Shah and Ajmal, Mian},
    booktitle={European Conference on Computer Vision {ECCV}},
    year={2024}
}