Search Results for author:
Found 7 papers, 6 papers with code
A principled approach to model validation in domain generalization
Domain generalization aims to learn a model with good generalization ability, that is, the learned model should not only perform well on several seen domains but also on unseen domains with different data distributions.
Trade-off between reconstruction loss and feature alignment for domain generalization
To deal with challenging settings in DG where both data and label of the unseen domain are not available at training time, the most common approach is to design the classifiers based on the domain-invariant representation features, i. e., the latent representations that are unchanged and transferable between domains.
Joint covariate-alignment and concept-alignment: a framework for domain generalization
Particularly, our framework proposes to jointly minimize both the covariate-shift as well as the concept-shift between the seen domains for a better performance on the unseen domain.
Conditional entropy minimization principle for learning domain invariant representation features
Invariance-principle-based methods such as Invariant Risk Minimization (IRM), have recently emerged as promising approaches for Domain Generalization (DG).
Barycentric-alignment and reconstruction loss minimization for domain generalization
To bridge this gap between theory and practice, we introduce a new upper bound that is free of terms having such dual dependence, resulting in a fully optimizable risk upper bound for the unseen domain.
Soft and subspace robust multivariate rank tests based on entropy regularized optimal transport
In this paper, we extend the recently proposed multivariate rank energy distance, based on the theory of optimal transport, for statistical testing of distributional similarity, to soft rank energy distance.
Domain Adaptation for Robust Workload Level Alignment Between Sessions and Subjects using fNIRS
Results: In a sample of six subjects, G-W resulted in an alignment accuracy of 68 $\pm$ 4 % (weighted mean $\pm$ standard error) for session-by-session alignment, FG-W resulted in an alignment accuracy of 55 $\pm$ 2 % for subject-by-subject alignment.
