Bringing balance to hand shape classification: Mitigating data imbalance through generative models

Journal paper

This paper improves hand-shape classification in sign language by using GAN-generated images for pre-training. This boosts accuracy (up to 5% overall, 100% on rare classes), halves training time, and enables cross-dataset generalization. Pre-training with synthetic data outperforms other augmentation methods.