low-resource-learning

This paper proposes a coverage-aware virtual IMU augmentation framework to address data scarcity in human activity recognition, generating and selecting synthetic sensor data to improve model performance with limited labeled data.

Presents a framework for financial sentiment analysis using distillation with synthetic data, transferring knowledge from a large teacher to compact student models, with clustering-based seed selection for efficient low-resource domain adaptation.