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Dynamic Instance Domain Adaptation
Zhongying Deng
*
,
Kaiyang Zhou
, Da Li
, Junjun He
, Yi Zhe Song
, Tao Xiang
*
Corresponding author for this work
Department of Computer Science
Research output
:
Contribution to journal
›
Journal article
›
peer-review
44
Citations (Scopus)
Overview
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Dive into the research topics of 'Dynamic Instance Domain Adaptation'. Together they form a unique fingerprint.
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Keyphrases
Feature Alignment
100%
Domain Adaptation
100%
Unsupervised Domain Adaptation
100%
Domain Name
66%
Training Samples
33%
Target Domain
33%
Semi-supervised Learning
33%
Source Domain
33%
Domain Data
33%
Learning Paradigm
33%
Art Performance
33%
Modern Art
33%
Alignment Loss
33%
Domain-agnostic
33%
Domain-invariant Feature
33%
Cross-entropy Loss
33%
Feature Distribution Alignment
33%
Instance-adaptive
33%
Adaptive Convolutional Kernels
33%
Classic Style
33%
Painting Style
33%
Dynamic Neural Network
33%
Domain Annotation
33%
Computer Science
Domain Adaptation
100%
Unsupervised Domain Adaptation
100%
Multi-Source
66%
Training Sample
33%
Semisupervised Learning
33%
Annotation
33%
Neural Network
33%
Source Domain Data
33%
Target Domain Data
33%
Deep Feature
33%
Art Performance
33%
Invariant Domain
33%
Picture Archiving and Communication System (Medical Imaging)
33%