Shun-Feng Su

C-Score: Beyond Accuracy for Robustness Assessment in Semi-Supervised Learning under Open-World Unlabeled Contamination

C-Score, a compact framework that evaluates training behavior in three complementary spaces: prediction, feature representation, and optimization, suggests that clean accuracy alone is insufficient for evaluating SSL robustness in open-world environments, and that internal diagnostic signals are necessary for more reliable robustness assessment under unlabeled contamination.