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<script src="http://www.google.com/jsapi" type="text/javascript"></script>
<script type="text/javascript">google.load("jquery", "1.3.2");</script>
<style type="text/css">
body {
font-family: "HelveticaNeue-Light", "Helvetica Neue Light", "Helvetica Neue", Helvetica, Arial, "Lucida Grande", sans-serif;
font-weight:300;
font-size:18px;
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</style>
<html>
<head>
<title>Make Me a BNN</title>
<meta property="og:image" content="Path to my teaser.png"/> <!-- Facebook automatically scrapes this. Go to https://developers.facebook.com/tools/debug/ if you update and want to force Facebook to rescrape. -->
<meta property="og:title" content="Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty from Pre-trained Models." />
<meta property="og:description" content="Paper description." />
<!-- Get from Google Analytics -->
<!-- Global site tag (gtag.js) - Google Analytics -->
<script async src=""></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-75863369-6');
</script>
</head>
<body>
<br>
<center>
<span style="font-size:36px">Make Me a BNN: A Simple Strategy for Estimating
Bayesian Uncertainty from Pre-trained Models</span>
<table align=center width=600px>
<table align=center width=600px>
<tr>
<td align=center width=100px>
<center>
<span style="font-size:24px"><a href="https://giannifranchi.github.io/">Gianni Franchi</a></span>
</center>
</td>
<td align=center width=100px>
<center>
<span style="font-size:24px"><a href="https://scholar.google.fr/citations?user=RW4CQ68AAAAJ&hl=fr">Olivier Laurent</a></span>
</center>
</td>
<td align=center width=100px>
<center>
<span style="font-size:24px"><a href="">Maxence Leguery</a></span>
</center>
</td>
<td align=center width=100px>
<center>
<span style="font-size:24px"><a href="https://abursuc.github.io/">Andrei Bursuc</a></span>
</center>
</td>
<td align=center width=100px>
<center>
<span style="font-size:24px"><a href="https://scholar.google.it/citations?user=zooORRsAAAAJ&hl=it">Andrea Pilzer</a></span>
</center>
</td>
<td align=center width=100px>
<center>
<span style="font-size:24px"><a href="https://www.comp.nus.edu.sg/~ayao/">Angela Yao</a></span>
</center>
</td>
</tr>
</table>
<table align=center width=250px>
<tr>
<td align=center width=120px>
<center>
<span style="font-size:24px"><a href='https://arxiv.org/abs/2312.15297'>[Paper]</a></span>
</center>
</td>
<td align=center width=120px>
<center>
<span style="font-size:24px"><a href='https://torch-uncertainty.github.io/'>[GitHub]</a></span><br>
</center>
</td>
</tr>
</table>
</table>
</center>
<center>
<table align=center width=850px>
<tr>
<td width=260px>
<center>
<img class="round" style="width:500px" src="./resources/process_abnn.png"/>
</center>
</td>
</tr>
</table>
<table align=center width=850px>
<tr>
<td>
Illustration of the training process for the ABNN. The procedure begins with training a single DNN omega (w) MAP ,followed by architectural adjustments to transform it into an ABNN. The final step involves fine-tuning the ABNN model.
</td>
</tr>
</table>
</center>
<hr>
<table align=center width=850px>
<center><h1>Abstract</h1></center>
<tr>
<td>
Deep Neural Networks (DNNs) are powerful tools for various computer vision tasks, yet they often struggle with reliable uncertainty quantification — a critical requirement for real-world applications. Bayesian Neural Networks (BNN) are equipped for uncertainty estimation but cannot scale to large DNNs where they are highly unstable to train.
To address this challenge, we introduce the Adaptable Bayesian Neural Network (ABNN), a simple and scalable strategy to seamlessly transform DNNs into BNNs in a post-hoc manner with minimal computational and training overheads.
ABNNpreserves the main predictive properties of DNNs while enhancing their uncertainty quantification abilities through simple BNN adaptation layers (attached to normalization layers) and a few fine-tuning steps on pre-trained models. We conduct extensive experiments across multiple datasets for image classification and semantic segmentation tasks, and our results demonstrate that ABNN achieves state-of-the-art performance without the computational budget typically associated with ensemble methods.
</td>
</tr>
</table>
<br>
<hr>
<center><h1>Talk</h1>
<!-- <iframe id="player" type="text/html" width="640" height="360"
src="http://www.youtube.com/embed/aXqVBAOXc0o"
frameborder="0">
</iframe> -->
<iframe width="560" height="315" src="https://www.youtube.com/embed/aXqVBAOXc0o?si=IcealsiKVr5ky_U9" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</center>
<table align=center width=800px>
<br>
<tr>
<center>
<span style="font-size:28px"><a href="./resources/CVPR-presentation_ABNN.pdf"/>[Slides]</a>
</span>
</center>
</tr>
</table>
<hr>
<br>
<hr>
<table align=center width=450px>
<center><h1>Paper and Supplementary Material</h1></center>
<tr>
<td><a href=""><img class="layered-paper-big" style="height:175px" src="./resources/paper.png"/></a></td>
<td><span style="font-size:14pt">G. Franchi, O. Laurent, M. Leguery, A. Bursuc, A. Pilzer, A. Yao<br>
<b>Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty from Pre-trained Models.</b><br>
In IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2024.<br>
(hosted on <a href="https://arxiv.org/html/2312.15297v1">ArXiv</a>)<br>
<!-- (<a href="./resources/camera-ready.pdf">camera ready</a>)<br> -->
<span style="font-size:4pt"><a href=""><br></a>
</span>
</td>
</tr>
</table>
<br>
<table align=center width=600px>
<tr>
<td><span style="font-size:14pt"><center>
<a href="./resources/bibtex.txt">[Bibtex]</a>
</center></td>
</tr>
</table>
<hr>
<br>
<table align=center width=900px>
<tr>
<td width=400px>
<left>
<center><h1>Acknowledgements</h1></center>
This template was originally made by <a href="http://web.mit.edu/phillipi/">Phillip Isola</a> and <a href="http://richzhang.github.io/">Richard Zhang</a> for a <a href="http://richzhang.github.io/colorization/">colorful</a> ECCV project; the code can be found <a href="https://github.com/richzhang/webpage-template">here</a>.
</left>
</td>
</tr>
</table>
<br>
</body>
</html>