forked from jasonlong/cayman-theme
-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathindex.html
64 lines (53 loc) · 3.26 KB
/
index.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
<!DOCTYPE html>
<html lang="en-us">
<head>
<meta charset="UTF-8">
<title>UniOT-for-UniDA</title>
<link rel="icon" href="shooting-star_emoji.png" title="UniOT-for-UniDA">
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="theme-color" content="#157878">
<link rel="stylesheet" href="css/normalize.css">
<link href='https://fonts.googleapis.com/css?family=Open+Sans:400,700' rel='stylesheet' type='text/css'>
<link rel="stylesheet" href="css/cayman.css">
</head>
<body>
<section class="page-header">
<h1 class=>NeurIPS 2022 Spotlight</h1>
<h1 class="project-name">Unified Optimal Transport Framework<br>for Universal Domain Adaptation</h1>
<h2 class="project-tagline">Wanxing Chang<sup>1</sup>   Ye Shi<sup>1,</sup>*   Hoang Duong Tuan<sup>2</sup>   Jingya Wang<sup>1,</sup>*</h2>
<h2 class="project-tagline"><sup>1</sup>ShanghaiTech University   <sup>2</sup>University of Technology Sydney</h2>
<a href="https://openreview.net/forum?id=RTan64GlCLV" class="btn">paper</a>
<a href="https://github.com/changwxx/UniOT-for-UniDA" class="btn">code</a>
<a href="https://nips.cc/media/neurips-2022/Slides/53416_5dK0UW8.pdf" class="btn">slides</a>
<a href="https://nips.cc/media/PosterPDFs/NeurIPS%202022/53416.png" class="btn">poster</a>
<!-- <br> -->
<a href="https://youtu.be/RwogtOEUPQQ" class="btn">video (English)</a>
<a href="https://www.bilibili.com/video/BV1NM411L7BB" class="btn">video (Chinese)</a>
<a href="https://zhuanlan.zhihu.com/p/637401530" class="btn">blog (Chinese)</a>
</section>
<section class="main-content">
<p>In this paper, we propose to use Optimal Transport to handle common class detection and private class discovery for UniDA under a unified framework, namely UniOT. </p>
<p><img src="UniOT-pipeline-des.png" alt="" style="max-width:100%;"></p>
<h1>
<a id="user-content-header-1" class="anchor" href="#header-1" aria-hidden="true"><span class="octicon octicon-link"></span></a>Video</h1>
<div class="video"><iframe class="video" frameborder="0" width="100%" height="500"
src="https://www.youtube.com/embed/RwogtOEUPQQ">
</iframe></div>
<h1>
<a id="user-content-header-1" class="anchor" href="#header-1" aria-hidden="true"><span class="octicon octicon-link"></span></a>Cite our work</h1>
If you find this work useful in your research, please consider citing:
<p><div class="highlight highlight-Javascript"><pre>@inproceedings{chang2022unified,
author = {Chang, Wanxing and Shi, Ye and Tuan, Hoang and Wang, Jingya},
booktitle = {Advances in Neural Information Processing Systems},
pages = {29512--29524},
title = {Unified Optimal Transport Framework for Universal Domain Adaptation},
volume = {35},
year = {2022}
}</pre></div></p>
<footer class="site-footer">
<!-- <span class="site-footer-owner"><a href="https://github.com/jasonlong/cayman-theme">Cayman</a> is maintained by <a href="https://github.com/jasonlong">jasonlong</a>.</span> -->
<span class="site-footer-credits">This page was generated by <a href="https://pages.github.com">GitHub Pages</a>.</span>
</footer>
</section>
</body>
</html>