Papers Evolving Domain Generalization
“Evolving Domain Generalization” 태그가 달린 논문 4편 · 필터 해제
Enhancing Evolving Domain Generalization through Dynamic Latent Representations
Domain generalization is a critical challenge for machine learning systems. Prior domain generalization methods focus on extracting domain-invariant features across several stationary domains to enable generalization to …
Domain GeneralizationEvolving Domain GeneralizationForesee What You Will Learn: Data Augmentation for Domain Generalization in Non-stationary Environment
Existing domain generalization aims to learn a generalizable model to perform well even on unseen domains. For many real-world machine learning applications, the data distribution often shifts gradually along domain indi…
Data AugmentationDomain GeneralizationEvolving Domain GeneralizationMeta-LearningEvolving Domain Generalization
Domain generalization aims to learn a predictive model from multiple different but related source tasks that can generalize well to a target task without the need of accessing any target data. Existing domain generalizat…
Domain GeneralizationEvolving Domain GeneralizationMeta-LearningGeneralizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder
Domain generalization aims to improve the generalization capability of machine learning systems to out-of-distribution (OOD) data. Existing domain generalization techniques embark upon stationary and discrete environment…
Domain GeneralizationEvolving Domain Generalization