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Found 4 papers, 3 papers with code
Parameter Exchange for Robust Dynamic Domain Generalization
The parameters of dynamic networks can be decoupled into a static and a dynamic component, which are designed to learn domain-invariant and domain-specific features, respectively.
Periodically Exchange Teacher-Student for Source-Free Object Detection
To address this issue, we propose the Periodically Exchange Teacher-Student (PETS) method, a simple yet novel approach that introduces a multiple-teacher framework consisting of a static teacher, a dynamic teacher, and a student model.
MetaFBP: Learning to Learn High-Order Predictor for Personalized Facial Beauty Prediction
To this end, we propose a novel MetaFBP framework, in which we devise a universal feature extractor to capture the aesthetic commonality and then optimize to adapt the aesthetic individuality by shifting the decision boundary of the predictor via a meta-learning mechanism.
Dynamic Domain Generalization
Specifically, we leverage a meta-adjuster to twist the network parameters based on the static model with respect to different data from different domains.
Ranked #18 on
Domain Generalization
on DomainNet
