Learning Robust Shape-Based Features for Domain Generalization
Domain generalization is a challenging problem of learning models that can generalize to novel testing domains which are unavailable during training and follow different distributions from training domains. In this paper, we introduce a simple but effective method for domain generalization, which is...
Main Authors: | Yexun Zhang, Ya Zhang, Qinwei Xu, Ruipeng Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9050708/ |
Similar Items
-
Learning Domain-Specific Features From General Features for Person Re-Identification
by: Chengqiu Dai, et al.
Published: (2020-01-01) -
Multi-Domain Feature Alignment for Face Anti-Spoofing
by: Shizhe Zhang, et al.
Published: (2023-04-01) -
Learning Robust Shape-Indexed Features for Facial Landmark Detection
by: Xintong Wan, et al.
Published: (2022-06-01) -
Less Is More: Robust and Novel Features for Malicious Domain Detection
by: Chen Hajaj, et al.
Published: (2022-03-01) -
Inter-domain curriculum learning for domain generalization
by: Daehee Kim, et al.
Published: (2022-06-01)