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o-laurent committed May 3, 2024
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<section class="page-header">
<h1 class="project-name">A Bayesian Odyssey in Uncertainty: from Theoretical
Foundations to Real-World Applications</h1>
<h2 class="project-tagline">ECCV 2024<br>TBA</h2>
<h2 class="project-tagline">ECCV 2024<br>To Be Announced</h2>
</section>

<section class="main-content" style="max-width:100%">
Expand Down Expand Up @@ -73,26 +73,79 @@ <h2>Organizers</h2>
<a href="https://abursuc.github.io/" target="_blank">
<div class="instructorphoto"><img src="https://abursuc.github.io/img/abursuc.jpg">
</div>
<div>Andrei Bursuc<br><small>Valeo.ai</small></div>
<div>Andrei Bursuc<br><small>valeo.ai</small></div>
</a>
</div>

<br>
<div class="containertext" style="max-width:50rem">
<h2 style="text-align: center">Overview</h2>
<div style="text-align: center">TBA</div>
<div style="text-align: center">
This tutorial is here to help researchers understand and handle uncertainty in their models,
making them more reliable using Bayesian methods. We'll start by discussing different Bayesian
approaches and then focus on Bayesian Neural Networks and how to approximate them efficiently for
computer vision tasks. We will also use real-world examples and practical methods to show how to
put these ideas into practice.
</div>
</div>

<br>

<div class="containertext" style="max-width:50rem">
<h2 style="text-align: center">Outline</h2>
<div style="text-align: center">TBA</div>

<h3 style="text-align: left">Uncertainty quantification framework</h3>
<p> In this section, we will quickly introduce the <a
href="https://github.com/ensta-u2is-ai/torch-uncertainty">TorchUncertainty
library</a>, an uncertainty-aware open-source framework for training models in PyTorch.</p>
<h3 style="text-align: left">Introduction: Why & where is UQ helpful?</h3>
<p>
Initial exploration into the critical role of uncertainty quantification (UQ) within the realm
of computer vision (CV): participants will gain an understanding of why it’s essential to consider uncertainty in CV, especially concerning decision-making in complex
environments. We will introduce real-world scenarios where uncertainty can profoundly
impact model performance and safety, setting the stage for deeper exploration through out the tutorial.
</p>
<h3 style="text-align: left">From maximum a posteriori to BNNs.</h3>
<p>
In this part, we will journey through the evolution of UQ techniques, starting
from classic approaches such as maximum a posteriori estimation to the more ellaborate Bayesian Neural Networks. The participants will grasp the conceptual foundations
of UQ, laying the groundwork for the subsequent discussions of Bayesian methods.
</p>
<h3 style="text-align: left">Strategies for BNN posterior inference.</h3>
<p>
This is the core part, which will dive into the process of estimating the posterior distribution of BNNs. The participants
will gain insights into the computational complexities involved in modeling uncertainty
through a comprehensive overview of techniques such as Variational Inference (VI),
Hamiltonian Monte Carlo (HMC), and Langevin Dynamics. Moreover, we will explore
the characteristics and visual representation of posterior distributions, providing a better
understanding of Bayesian inference.
</p>
<h3 style="text-align: left">Computationally-efficient BNNs for CV.</h3>
<p>
Here, we will present recent techniques to improve the computational efficiency of BNNs for computer vision tasks.
We will present different forms of obtaining BNNs from a intermediate checkpoints,
weight trajectories during a training run, different types of variational subnetworks,
etc., along with their main strenghts and limitations.
</p>
<h3 style="text-align: left">Convert your DNN into a BNN: post-hoc BNN inference.</h3>
<p>
This segment focuses on post-hoc inference techniques, with a focus on Laplace approximation. The participants
will learn how Laplace approximation serves as a computationally efficient method for
approximating the posterior distribution of Bayesian Neural Networks.
</p>
<h3 style="text-align: left">Quality of estimated uncertainty and practical examples.</h3>
<p>
In the final session, participants will learn how to evaluate the quality of UQ in practi-
cal settings. We will develop multiple approaches to assess the reliability and calibra-
tion of uncertainty estimates, equipping participants with the tools to gauge the robust-
ness of their models. Additionally, we will dive into real-world examples and applica-
tions, showcasing how UQ can enhance the reliability
and performance of computer vision systems in diverse scenarios. Through interactive
discussions and case studies, participants will gain practical insights into deploying
uncertainty-aware models in real-world applications.
</p>

<h3 style="text-align: left">Uncertainty Quantification Framework.</h3>
<p>
This tutorial will also very quickly introduce the <a href="https://github.com/ensta-u2is-ai/torch-uncertainty">TorchUncertainty
library</a>, an uncertainty-aware open-source framework for training models in PyTorch.
</p>
</div>

<a href="https://torch-uncertainty.github.io/" target="_blank">
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<div class="containertext" style="max-width:50rem">
<h2 style="text-align: center">Relation to prior tutorials and short courses</h2>
<p> This tutorial is affiliated with the <a href="https://uncv2023.github.io/">UNCV workshop</a>,
which had its inaugural edition at ECCV and the subsequent one at ICCV, although our primary
emphasis in this tutorial will be on the theoretical facets. </p>
<p> Uncertainty Quantification has received some attention
in recent times, as evidenced by its inclusion as sections in
<p> This tutorial is affiliated with the <a href="https://uncv2023.github.io/">UNCV Workshop</a>,
which had its inaugural edition at ECCV 2022, a subsequent one at ICCV, and is back at ECCV this year.
In constrast to the workshop, the tutorial puts its primary emphasis on the theoretical facets. </p>
<p> UQ has received some attention
in recent times, as evidenced by its inclusion in
the tutorial <a href="https://abursuc.github.io/many-faces-reliability/">'Many Faces of Reliability of Deep
Learning for Real-World Deployment'</a>. While this excellent
tutorial explored various applications associated with uncertainty, it did not place a specific emphasis on
probabilistic
models and Bayesian Neural Networks. Our tutorial aims
Learning for Real-World Deployment'</a>. While this tutorial explored various applications associated with
uncertainty,
it did not place a specific emphasis on probabilistic models and Bayesian Neural Networks. Our tutorial aims
to provide a more in-depth exploration of uncertainty theory, accompanied by the introduction of practical
applications, including the presentation of the library, <a
href="https://github.com/ensta-u2is-ai/torch-uncertainty">TorchUncertainty</a>.</p>
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2 changes: 1 addition & 1 deletion wacv_2024.html
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Expand Up @@ -70,7 +70,7 @@ <h2>Organizers</h2>

<div class="instructorphoto"><img src="https://abursuc.github.io/img/abursuc.jpg" width="20%" hspace="2%">
</div>
<div>Andrei Bursuc<br><small>Valeo.ai</small></div>
<div>Andrei Bursuc<br><small>valeo.ai</small></div>
</a>
</div>

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