Papers Zero-Shot Learning + Domain Generalization
“Zero-Shot Learning + Domain Generalization” 태그가 달린 논문 4편 · 필터 해제
BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning
Despite the success of deep neural networks, there are still many challenges in deep representation learning due to the data scarcity issues such as data imbalance, unseen distribution, and domain shift. To address the a…
Compositional Zero-Shot LearningContrastive LearningDomain GeneralizationLong-tail Learning+3Context-Conditional Adaptation for Recognizing Unseen Classes in Unseen Domains
Recent progress towards designing models that can generalize to unseen domains (i.e domain generalization) or unseen classes (i.e zero-shot learning) has embarked interest towards building models that can tackle both dom…
Domain GeneralizationZero-Shot LearningZero-Shot Learning + Domain GeneralizationStructured Latent Embeddings for Recognizing Unseen Classes in Unseen Domains
The need to address the scarcity of task-specific annotated data has resulted in concerted efforts in recent years for specific settings such as zero-shot learning (ZSL) and domain generalization (DG), to separately addr…
Domain GeneralizationZero-Shot LearningZero-Shot Learning + Domain GeneralizationTowards Recognizing Unseen Categories in Unseen Domains
Current deep visual recognition systems suffer from severe performance degradation when they encounter new images from classes and scenarios unseen during training. Hence, the core challenge of Zero-Shot Learning (ZSL) i…
Domain GeneralizationZero-Shot LearningZero-Shot Learning + Domain Generalization