On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering
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Self-supervised learning is a central component in recent approaches to deep multi-view clustering (MVC). However, we find large variations in the development of self-supervision-based methods for deep MVC, potentially slowing the progress of the field. To address this, we present Deep-MVC, a unified framework for deep MVC that includes many recent methods as instances. We leverage our framework to make key observations about the effect of self-supervision, and in particular, drawbacks of aligning representations with contrastive learning. Further, we prove that contrastive alignment can negatively influence cluster separability, and that this effect becomes worse when the number of views increases. Motivated by our findings, we develop several new DeepMVC instances with new forms of self-supervision. We conduct extensive experiments and find that (i) in line with our theoretical findings, contrastive alignments decreases performance on datasets with many views; (ii) all methods benefit from some form of self-supervision; and (iii) our new instances outperform previous methods on several datasets. Based on our results, we suggest several promising directions for future research. To enhance the openness of the field, we provide an open-source implementation of DeepMVC, including recent models and our new instances. Our implementation includes a consistent evaluation protocol, facilitating fair and accurate evaluation of methods and components11Code: https://github.com/DanielTrosten/DeepMVC.
Originalsprog | Engelsk |
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Titel | Proceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023 |
Antal sider | 10 |
Forlag | IEEE Computer Society Press |
Publikationsdato | 2023 |
Sider | 23976-23985 |
ISBN (Elektronisk) | 9798350301298 |
DOI | |
Status | Udgivet - 2023 |
Begivenhed | 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023 - Vancouver, Canada Varighed: 18 jun. 2023 → 22 jun. 2023 |
Konference
Konference | 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023 |
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Land | Canada |
By | Vancouver |
Periode | 18/06/2023 → 22/06/2023 |
Sponsor | Amazon Science, Ant Research, Cruise, et al., Google, Lambda |
Bibliografisk note
Funding Information:
This work was financially supported by the Research Council of Norway (RCN), through its Centre for Research-based Innovation funding scheme (Visual Intelligence, grant no. 309439), and Consortium Partners. It was further funded by RCN FRIPRO grant no. 315029, RCN IKTPLUSS grant no. 303514, and the UiT Thematic Initiative “Data-Driven Health Technology”.
Publisher Copyright:
© 2023 IEEE.
ID: 371288803