Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis
2021
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:
https://github.com/fel-thomas/Sobol-Attribution-Method
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