Abstract
Insufficient and imbalance data samples often prevent the development of accurate deep learning models for manufacturing defect detection. By applying data augmentation methods ‐ including VAE latent space oversampling and random data generation, and GAN multi‐modal complementary data generation, we overcome the dataset limitations and achieve Pass/No‐Pass accuracies of over 90%.
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