DANCE: Enhancing saliency maps using decoys

TL;DR

This paper introduces DANCE, a framework that enhances the robustness and interpretability of saliency maps in neural networks by using input perturbations and aggregation, effectively countering issues like gradient saturation and adversarial attacks.

Contribution

The paper proposes a novel two-step approach combining input perturbations and saliency map aggregation to improve interpretability and robustness of saliency methods.

Findings

Outperforms existing saliency methods qualitatively.

Demonstrates robustness against adversarial perturbations.

Captures inter-feature dependence effectively.

Abstract

Saliency methods can make deep neural network predictions more interpretable by identifying a set of critical features in an input sample, such as pixels that contribute most strongly to a prediction made by an image classifier. Unfortunately, recent evidence suggests that many saliency methods poorly perform, especially in situations where gradients are saturated, inputs contain adversarial perturbations, or predictions rely upon inter-feature dependence. To address these issues, we propose a framework that improves the robustness of saliency methods by following a two-step procedure. First, we introduce a perturbation mechanism that subtly varies the input sample without changing its intermediate representations. Using this approach, we can gather a corpus of perturbed data samples while ensuring that the perturbed and original input samples follow the same distribution. Second, we compute saliency maps for the perturbed samples and propose a new method to aggregate saliency maps. With this design, we offset the gradient saturation influence upon interpretation. From a theoretical perspective, we show the aggregated saliency map could not only capture inter-feature dependence but, more importantly, robustify interpretation against previously described adversarial perturbation methods. Following our theoretical analysis, we present experimental results suggesting that, both qualitatively and quantitatively, our saliency method outperforms existing methods.