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Taemin Park

2 accepted papers

2025

Learnable Logit Adjustment for Imbalanced Semi-Supervised Learning under Class Distribution Mismatch

ICCV 2025poster

In class-imbalanced learning (CIL), post-hoc logit adjustment (LA) effectively mitigates class imbalance by adjusting biased logits according to label frequencies. Given the success of LA in CIL, recent class-imbalanced semi-supervised learning (CISSL) algorithms incorporated LA, leading to improved…

Cited by 0SourcePDFScholar
2024

Rebalancing Using Estimated Class Distribution for Imbalanced Semi-Supervised Learning under Class Distribution Mismatch

ECCV 2024poster

"Despite significant advancements in class-imbalanced semi-supervised learning (CISSL), many existing algorithms explicitly or implicitly assume that the class distribution of unlabeled data matches that of labeled data. However, when this assumption fails in practice, the classification performance…