Rethinking multi-view representation learning via distilled disentangling

Rethinking multi-view representation learning via distilled disentangling

Multi-view representation learning aims to derive robust representations that are both view-consistent and view-specific from diverse data sources. This paper presents an in-depth analysis of existing approaches in this domain, highlighting a commonly overlooked aspect: the redundancy between view-c...

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Bibliographic Details
Main Authors: KE, Guanzhou, WANG, Bo, WANG, Xiaoli, HE, Shengfeng
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2024
Subjects:
Representation learning
Computer vision
Filters
Codes
Soft sensors
Redundancy
Pattern recognition
Multi-view representation learning
Artificial Intelligence and Robotics
Graphics and Human Computer Interfaces
Online Access:https://ink.library.smu.edu.sg/sis_research/9777
https://ink.library.smu.edu.sg/context/sis_research/article/10777/viewcontent/2403.10897v2.pdf
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/sis_research/9777
https://ink.library.smu.edu.sg/context/sis_research/article/10777/viewcontent/2403.10897v2.pdf

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