Learning Generalizable Models via Disentangling Spurious and Enhancing Potential Correlations-Reference-Cited by-同舟云学术

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Learning Generalizable Models via Disentangling Spurious and Enhancing Potential Correlations

Published:2024 Issue: Volume:33 Page:1627-1642
ISSN:1057-7149
Container-title:IEEE Transactions on Image Processing
language:
Short-container-title:IEEE Trans. on Image Process.

Author:

Wang Na¹^ORCID,Qi Lei²^ORCID,Guo Jintao¹^ORCID,Shi Yinghuan¹^ORCID,Gao Yang¹^ORCID

Affiliation:

1. National Key Laboratory for Novel Software Technology and the National Institute of Healthcare Data Science, Nanjing University, Nanjing, China

2. School of Computer Science and Engineering and the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Ministry of Education, Southeast University, (Southeast University), Nanjing, China

Funder

NSFC Program

Jiangsu Natural Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/83/10346232/10430110.pdf?arnumber=10430110

Reference70 articles.

1. Improving Vision Transformers by Revisiting High-Frequency Components

2. RUBi: Reducing unimodal biases for visual question answering;Cadène

3. Frequency Domain Image Translation: More Photo-realistic, Better Identity-preserving

4. Domain Generalization by Solving Jigsaw Puzzles

5. SWAD: Domain generalization by seeking flat minima;Cha

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