Information-theoretic regularization for Multi-source Domain Adaptation-Reference-Cited by-同舟云学术

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Information-theoretic regularization for Multi-source Domain Adaptation

Published:2021-10 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE/CVF International Conference on Computer Vision (ICCV)
language:
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Author:

Park Geon Yeong¹,Wan Lee Sang¹

Affiliation:

1. KAIST,Daejeon,South Korea

Funder

Samsung

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9709627/9709628/09711443.pdf?arnumber=9711443

Reference48 articles.

1. Deep learning and the information bottleneck principle

2. The information bottleneck method;tishby,2000

3. Adversarial dropout regularization;saito,2017

4. Adapting visual category models to new domains;saenko;European Conference on Computer Vision,2010

5. Fr-train: A mutual information-based approach to fair and robust training;roh,2020

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3. Multi-Source Domain Adaptation with Mixture of Joint Distributions;Pattern Recognition;2024-05

4. Training multi-source domain adaptation network by mutual information estimation and minimization;Neural Networks;2024-03

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