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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;
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<html>
<head>
<title>Task-Free Continual Learning via Online Discrepancy Distance Learning</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="Creative and Descriptive Paper Title." />
<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">Task-Free Continual Learning via Online Discrepancy Distance Learninge</span>
<br>
</br>
<span style="font-size:26px;color:black">NeurIPS 2022</span>
</br>
<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://fy6897.wixsite.com/feiye">Fei Ye</a></span>
</center>
</td>
<td align=center width=100px>
<center>
<span style="font-size:24px"><a href="https://www-users.cs.york.ac.uk/adrian/">Adrian G. Bors</a></span>
</center>
</td>
</tr>
</table>
</br>
<table align=center width=250px>
<tr>
<center>
Department of Computer Science,
University of York,
York, YO10 5GH, UK,
fy689,adrian.bors@york.ac.uk
</center>
</tr>
</table>
</br>
<table align=center width=250px>
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<td align=center width=120px>
<center>
<span style="font-size:24px"><a href=''>[Paper]</a></span>
</center>
</td>
<td align=center width=120px>
<center>
<span style="font-size:24px"><a href='https://github.com/dtuzi123/ODDL'>[GitHub]</a></span><br>
</center>
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</tr>
</table>
</table>
</center>
<center>
<table align=center width=850px>
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<center>
<img class="round" style="width:800px" src="./ODDL_newStructure.png"/>
</center>
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</tr>
</table>
<table align=center width=850px>
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<td>
The structure of the proposed model consisted of k components where each component has a classifier and VAE model. We only update the current component (k) in the training process. To check the model expansion, we generate the images for each component using the associated VAE model. Then those generated samples are used to estimate the discrepancy distance between the memory buffer and each previously learnt component. If the discrepancy distance is large, we expand the network architecture, otherwise, we perform the sample selection.
</td>
</tr>
</table>
</center>
<hr>
<table align=center width=850px>
<center><h1>Abstract</h1></center>
<tr>
<td>
<span style="font-size:20px">
Learning from non-stationary data streams, also called Task-Free Continual Learning (TFCL) remains challenging due to the absence of explicit task information. Although there are some recently proposed algorithms for TFCL, these methods lack theoretical guarantees. Moreover, there are no theoretical studies for forgetting analysis of TFCL. This paper develops a new theoretical analysis framework that derives generalization bounds based on the discrepancy distance between the visited samples and the entire information made available for training the model. This analysis provides new insights into the forgetting behaviour in classification tasks. Inspired by this theoretical model, we propose a new approach enabled with the dynamic component expansion mechanism for a mixture model, namely Online Discrepancy Distance Learning (ODDL). ODDL estimates the discrepancy between the current memory and the already accumulated knowledge as the expansion signal to ensure a compact network architecture with optimal performance. We then propose a new sample selection approach that selectively stores the samples into the memory buffer through the discrepancy-based measure, further improving the performance. We perform several TFCL experiments with the proposed methodology, which demonstrate that the proposed approach achieves the state of the art performance.
</span>
</td>
</tr>
</table>
<br>
<hr>
<center><h1>Talk</h1></center>
<p align="center">
<iframe width="660" height="395" src="dada" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen align="center"></iframe>
</p>
<table align=center width=800px>
<br>
<tr>
<center>
<span style="font-size:28px"><a href=''>[Slides]</a>
</span>
</center>
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</table>
<hr>
<center><h1>Code</h1></center>
<table align=center width=420px>
<center>
<tr>
<td>
</td>
</tr>
</center>
</table>
<table align=center width=400px>
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<td><img class="round" style="width:800px" src="./algorithm.PNG"/></td>
</center>
</td>
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</table>
<table align=center width=850px>
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<td>
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<br>
<tr><center>
<span style="font-size:28px"> <a href='https://github.com/dtuzi123/ODDL'>[GitHub]</a>
</center>
</span>
</table>
<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">Fei Ye and Adrian G. Bors<br>
<b>Creative and Descriptive Paper Title.</b><br>
In Conference, 20XX.<br>
(hosted on <a href="">ArXiv</a>)<br>
<!-- (<a href="./resources/camera-ready.pdf">camera ready</a>)<br> -->
<span style="font-size:4pt"><a href=""><br></a>
</span>
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</table>
<br>
<table align=center width=600px>
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<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>
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<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>