Papers
1–4 of 4Semantics-Aware Generative Latent Data Augmentation for Learning in Low-Resource Domains
Despite strong performance in data-rich regimes, deep learning often underperforms in the data-scarce settings common in practice. While foundation models (FMs) trained on massive datasets demonstrate...
Diffusion-Based Data Augmentation for Image Recognition: A Systematic Analysis and Evaluation
Diffusion-based data augmentation (DiffDA) has emerged as a promising approach to improving classification performance under data scarcity. However, existing works vary significantly in task configura...
Data Augmentation via Causal-Residual Bootstrapping
Data augmentation integrates domain knowledge into a dataset by making domain-informed modifications to existing data points. For example, image data can be augmented by duplicating images in differen...
Topology-Preserving Data Augmentation for Ring-Type Polygon Annotations
Geometric data augmentation is widely used in segmentation pipelines and typically assumes that polygon annotations represent simply connected regions. However, in structured domains such as architect...