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    Semi-Supervised Learning With Wafer-Specific Augmentations for Wafer Defect Classification by Uk Jo, Seoung Bum Kim

    Published 2025-01-01
    “…Our approach achieves a macro F1-score of 0.841 with only 5% labeled data, surpassing state-of-the-art methods by 6.2% compared to WaPIRL and 7.5% compared to Manivannan’s method. Similarly, with 10%, 25%, and 50% labeled data, our method achieves F1-scores of 0.856, 0.874, and 0.891, respectively, showing improvements of 3.5%, 1.7%, and 1.2% over WaPIRL and 5.0%, 6.6%, and 11.9% over Manivannan’s method in each case. …”
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