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%.
Au- tosimulate: (quickly) learning synthetic data generation, in: Computer Vision – ECCV 2020, 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%.