TY - GEN
T1 - Semi-Supervised Domain Generalization with Graph-Based Classifier
AU - Ye, Minxiang
AU - Zhang, Yifei
AU - Zhu, Shiqiang
AU - Xie, Anhuan
AU - Xiang, Senwei
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Semi-supervised domain generalization (SSDG) has recently emerged as a potential research topic. Compared to domain generalization, SSDG represents a realistic and challenging goal, which only requires a few labels from source domains. To tackle this problem, this work presents a novel pseudo-labeling method that facilitates incremental learning on a large amount of unlabeled data. With edge weighting optimization, the proposed method utilizes the graph Laplacian regularizer (GLR) in a multi-class setting that relies on the generated similarity graph. The proposed overall SSDG scheme mitigates the overfitting problem by an adaptive threshold module based on a two-stage GLR denoiser. Our experiments on PACS and OfficeHome verify that the proposed method effectively improves the quality of pseudo-labeling and domain generalization, achieving top performance in terms of accuracy.
AB - Semi-supervised domain generalization (SSDG) has recently emerged as a potential research topic. Compared to domain generalization, SSDG represents a realistic and challenging goal, which only requires a few labels from source domains. To tackle this problem, this work presents a novel pseudo-labeling method that facilitates incremental learning on a large amount of unlabeled data. With edge weighting optimization, the proposed method utilizes the graph Laplacian regularizer (GLR) in a multi-class setting that relies on the generated similarity graph. The proposed overall SSDG scheme mitigates the overfitting problem by an adaptive threshold module based on a two-stage GLR denoiser. Our experiments on PACS and OfficeHome verify that the proposed method effectively improves the quality of pseudo-labeling and domain generalization, achieving top performance in terms of accuracy.
UR - https://www.scopus.com/pages/publications/86000387288
U2 - 10.1109/ICASSP49357.2023.10096357
DO - 10.1109/ICASSP49357.2023.10096357
M3 - 会议稿件
AN - SCOPUS:86000387288
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
BT - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Y2 - 4 June 2023 through 10 June 2023
ER -