Diffusion models have demonstrated competitive performance in missing data imputation
(MDI) task. However, directly applying diffusion models to MDI produces suboptimal …

Unsupervised Domain Adaptation (UDA) is quite challenging due to the large distribution
discrepancy between the source domain and the target domain. Inspired by diffusion models …

Large-scale diffusion models are adept at generating high-fidelity images and facilitating
image editing and interpolation. However, they have limitations when tasked with generating …

Empirical Risk Minimization (ERM) is fragile in scenarios with insufficient labeled samples. A
vanilla extension of ERM to unlabeled samples is Entropy Minimization (EntMin), which …

Recent advances achieved by deep learning models rely on the independent and identically
distributed assumption, hindering their applications in real-world scenarios with domain …

Unsupervised domain adaptation (UDA) offers a compelling solution to bridge the gap
between labelled synthetic data and unlabelled real‐world data for training semantic …

This paper introduces a novel unsupervised domain adaptation (UDA) method, MeTa
Discriminative Class-Wise MMD (MCWMMD), which combines meta-learning with a Class …

Meta-Learning aims at extracting shared knowledge (meta-knowledge) from historical tasks
to accelerate learning on new tasks. It has achieved promising performance in various …