DExT: Detector Explanation Toolkit
Deepan Chakravarthi Padmanabhan, Paul G. Pl\"oger, Octavio Arriaga,, Matias Valdenegro-Toro

TL;DR
DExT is an open-source toolkit that provides holistic, gradient-based explanations for object detector decisions, improving interpretability for safety-critical applications and enabling better evaluation of detector transparency.
Contribution
The paper introduces DExT, a comprehensive toolkit for explaining both bounding box and classification decisions of object detectors using gradient-based methods.
Findings
SSD is more faithfully explained than other detectors.
SmoothGrad with Guided Backpropagation offers the most trustworthy explanations.
DExT encourages interpretability evaluation of object detectors.
Abstract
State-of-the-art object detectors are treated as black boxes due to their highly non-linear internal computations. Even with unprecedented advancements in detector performance, the inability to explain how their outputs are generated limits their use in safety-critical applications. Previous work fails to produce explanations for both bounding box and classification decisions, and generally make individual explanations for various detectors. In this paper, we propose an open-source Detector Explanation Toolkit (DExT) which implements the proposed approach to generate a holistic explanation for all detector decisions using certain gradient-based explanation methods. We suggests various multi-object visualization methods to merge the explanations of multiple objects detected in an image as well as the corresponding detections in a single image. The quantitative evaluation show that the…
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Advanced Neural Network Applications · Adversarial Robustness in Machine Learning
