Label-Decoupled Style Augmentation for Domain Generalization in Multi-Label Remote Sensing Scene Classification
Multi-label classification assigns several co-occurring labels to each aerial scene, yet deployed models often encounter data distributions different from their training. Feature-statistics augmentation such as MixStyle, EFDMix, and correlated style uncertainty improves generalization at low cost but perturbs channel statistics globally, treating each image as a single style; one class can then contaminate the augmentation of another. Domain generalization is understudied for multi-label remote sensing; no prior method or multi-source benchmark targets it. A label-decoupled augmentation framew
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- PossiblePossibly related (embedding) · 45%satellite-image-deep-learning/model-training-and-deployment →
- LinkedLinked via arxiv author · 85%Alaa Almouradi →
“Label-Decoupled Style Augmentation for Domain Generalization in Multi-Label Remote Sensing Scene Classification”
- LinkedLinked via arxiv author · 85%Erchan Aptoula →
“Label-Decoupled Style Augmentation for Domain Generalization in Multi-Label Remote Sensing Scene Classification”