Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis

Thomas FEL, Remi Cadene, Mathieu Chalvidal, Matthieu Cord, David Vigouroux, Thomas Serre

Keywords: deep learning, machine learning, explainability, interpretability, computer vision, nlp, black box, sobol

TL;DR: We explain black-box models by measuring how their predictions vary when we perturb their input at various location using Sobol.

Abstract:

We describe a novel attribution method which is grounded in Sensitivity Analysis and uses Sobol indices. Beyond modeling the individual contributions of image regions, Sobol indices provide an efficient way to capture higher-order interactions between image regions and their contributions to a neural network's prediction through the lens of variance.
We describe an approach that makes the computation of these indices efficient for high-dimensional problems by using perturbation masks coupled with efficient estimators to handle the high dimensionality of images.
Importantly, we show that the proposed method leads to favorable scores on standard benchmarks for vision (and language models) while drastically reducing the computing time compared to other black-box methods -- even surpassing the accuracy of state-of-the-art white-box methods which require access to internal representations. Our code is freely available:
github.com/fel-thomas/Sobol-Attribution-Method.

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Code: https://github.com/fel-thomas/Sobol-Attribution-Method

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2111.04138/code)