Pre-training an EfficientNet classifier on GAN-generated balanced hand images, then fine-tuning on real data, raises accuracy on the imbalanced RWTH handshape benchmark from 80.6% to 85.3%.
Neural sign actors: A diffusion model for 3d sign language production from text, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp
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Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models
Pre-training an EfficientNet classifier on GAN-generated balanced hand images, then fine-tuning on real data, raises accuracy on the imbalanced RWTH handshape benchmark from 80.6% to 85.3%.