Combining Mean Teacher semi-supervised learning with supervised contrastive loss improves wafer map defect classification on WM811K by 4.5 to 6.7 percentage points over a ResNet18 baseline.
Advances in machine learning and deep learn- ing applications towards wafer map defect recognition and classification: a review,
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Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition
Combining Mean Teacher semi-supervised learning with supervised contrastive loss improves wafer map defect classification on WM811K by 4.5 to 6.7 percentage points over a ResNet18 baseline.