Contrastive Adaptation Network for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) makes predictions for the target domain data while manual annotations are only available in the source domain. Previous methods minimize the domain discrepancy neglecting the class information, which may lead to misalignment and poor generalization performance. T...

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Bibliographic Details
Main Authors: Kang, Guoliang, Jiang, Lu, Yang, Yi, Hauptmann, Alexander G.
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Annotations
Categorization
Computational modeling
Computer vision
Deep Learning
Manuals
Measurement
Recognition: Detection
Representation Learning
Retrieval
Training
Online Access:Request full text
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