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PointCloud.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="description" content="Explainability of Point Cloud Neural Networks Using SMILE">
<meta name="keywords" content="XAI, Explainability, SMILE, Point Cloud, SafeML">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Explainability with SMILE</title>
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<div class="container">
<h1 class="title">Explainability of Point Cloud Neural Networks Using SMILE</h1>
<p class="subtitle">Statistical Model-Agnostic Interpretability with Local Explanations</p>
<div class="content">
<img src="https://github.com/koo-ec/xwhy/blob/main/docs/graphics/XWhy_Logo_v1.png" alt="XWhy Logo">
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<section class="section">
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<h2 class="title is-3">Abstract</h2>
<p>This study explores the implementation of SMILE for Point Cloud offering enhanced robustness and interpretability, particularly when Anderson-Darling distance is used. The approach demonstrates superior performance in terms of fidelity loss, R<sup>2</sup> scores, and robustness across various kernel widths, perturbation numbers, and clustering configurations.</p>
<p>Additionally, a stability analysis using the Jaccard index establishes a benchmark for model stability in point cloud classification, identifying dataset biases crucial for safety-critical applications like autonomous driving.</p>
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</section>
<section class="section" id="BibTeX">
<div class="container">
<h2 class="title">BibTeX</h2>
<pre><code>
@article{aslansefat2024pointcloud,
title={Explainability of Point Cloud Neural Networks Using SMILE: Statistical Model-Agnostic Interpretability with Local Explanations},
author={Aslansefat, Koorosh and others},
journal={IEEE Software},
year={2024},
publisher={IEEE}
}
</code></pre>
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<p>Licensed under <a href="http://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a>. <a href="https://github.com/koo-ec/xwhy">Source Code</a>.</p>
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