Papers Source-free Domain Generalization
“Source-free Domain Generalization” 태그가 달린 논문 5편 · 필터 해제
BatStyler: Advancing Multi-category Style Generation for Source-free Domain Generalization
Source-Free Domain Generalization (SFDG) aims to develop a model that performs on unseen domains without relying on any source domains. However, the implementation remains constrained due to the unavailability of trainin…
Domain GeneralizationSource-free Domain GeneralizationTransfer LearningPromptTA: Prompt-driven Text Adapter for Source-free Domain Generalization
Source-free domain generalization (SFDG) tackles the challenge of adapting models to unseen target domains without access to source domain data. To deal with this challenging task, recent advances in SFDG have primarily …
Domain GeneralizationSource-free Domain GeneralizationDPStyler: Dynamic PromptStyler for Source-Free Domain Generalization
Source-Free Domain Generalization (SFDG) aims to develop a model that works for unseen target domains without relying on any source domain. Research in SFDG primarily bulids upon the existing knowledge of large-scale vis…
Domain GeneralizationSource-free Domain GeneralizationStyle TransferPromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization
In a joint vision-language space, a text feature (e.g., from "a photo of a dog") could effectively represent its relevant image features (e.g., from dog photos). Also, a recent study has demonstrated the cross-modal tran…
Domain GeneralizationImage ClassificationMulti-modal ClassificationMultimodal Deep Learning+4Domain-Unified Prompt Representations for Source-Free Domain Generalization
Domain generalization (DG), aiming to make models work on unseen domains, is a surefire way toward general artificial intelligence. Limited by the scale and diversity of current DG datasets, it is difficult for existing …
DiversityDomain GeneralizationSource-free Domain Generalization