Search Results for author:
Found 4 papers, 0 papers with code
PracticalDG: Perturbation Distillation on Vision-Language Models for Hybrid Domain Generalization
Domain Generalization (DG) aims to resolve distribution shifts between source and target domains, and current DG methods are default to the setting that data from source and target domains share identical categories.
Instance Paradigm Contrastive Learning for Domain Generalization
In this paper, we propose an instance paradigm contrastive learning framework, introducing contrast between original features and novel paradigms to alleviate domain-specific distractions.
Causality-based Dual-Contrastive Learning Framework for Domain Generalization
In this paper, we propose a Dual-Contrastive Learning (DCL) module on feature and prototype contrast.
Bag of Tricks for Out-of-Distribution Generalization
Recently, out-of-distribution (OOD) generalization has attracted attention to the robustness and generalization ability of deep learning based models, and accordingly, many strategies have been made to address different aspects related to this issue.
