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Cross-domain visual representations via unsupervised graph alignment
Baoyao Yang
,
Pong Chi YUEN
Department of Computer Science
Research output
:
Chapter in book/report/conference proceeding
›
Conference proceeding
›
peer-review
32
Citations (Scopus)
Overview
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Dive into the research topics of 'Cross-domain visual representations via unsupervised graph alignment'. Together they form a unique fingerprint.
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Keyphrases
Cross-domain
100%
Visual Representation
100%
Graph Alignment
100%
Alignment Method
75%
Distribution Structure
75%
Target Domain
50%
Differences in Distributions
50%
Distribution Alignment
25%
Adaptation
25%
Feature Space
25%
Source Domain
25%
Re-identification
25%
Data Distribution
25%
Data Representation
25%
Misclassification
25%
Cross-modal
25%
Cross-dataset Recognition
25%
Unsupervised Domain Adaptation
25%
Source Model
25%
Misidentification
25%
Unified Structure
25%
Cross-domain Representation
25%
Representational Structure
25%
Performance Drop
25%
Adversarial Network
25%
Computer Science
Visual Representation
100%
Experimental Result
25%
Good Performance
25%
Feature Space
25%
Reidentification
25%
Data Distribution
25%
Data Representation
25%
Unsupervised Domain Adaptation
25%
Domain Representation
25%