Data Augmentation Comparison Hub
4 papers - avg viability 5.5
Top Papers
- Semantics-Aware Generative Latent Data Augmentation for Learning in Low-Resource Domains(7.0)
GeLDA offers semantics-aware data augmentation to improve model performance in low-resource scenarios using generative techniques.
- Diffusion-Based Data Augmentation for Image Recognition: A Systematic Analysis and Evaluation(7.0)
A unified framework and codebase for diffusion-based data augmentation, enabling fair comparison and practical insights for low-data image classification.
- Data Augmentation via Causal-Residual Bootstrapping(4.0)
A novel data augmentation method that leverages causal knowledge to enhance predictive model accuracy.
- Topology-Preserving Data Augmentation for Ring-Type Polygon Annotations(4.0)
A novel approach to geometric data augmentation that preserves topological consistency in ring-type polygon annotations.