Supervised semantic segmentation has been widely utilized for the quality assurance of the
additive manufacturing process. However, abnormal states (those with defects) happen
significantly less frequently than normal ones (those without defects), resulting in the
collected dataset usually containing fewer defect samples than normal ones, which
deteriorates the performance of defect detection in additive manufacturing. To address this
issue, a novel contrastive learning-based semantic segmentation model, termed cLass …