3 papers
cs.LG2025
DiCaP: Distribution-Calibrated Pseudo-labeling for Semi-Supervised Multi-Label Learning
Bo Han, Zhuoming Li, Xiaoyu Wang +4
Semi-supervised multi-label learning (SSMLL) aims to address the challenge of limited labeled data in multi-label learning (MLL) by leveraging unlabeled data to improve the model's…
cs.CV2025
Keep It on a Leash: Controllable Pseudo-label Generation Towards Realistic Long-Tailed Semi-Supervised Learning
Yaxin Hou, Bo Han, Yuheng Jia +2
Current long-tailed semi-supervised learning methods assume that labeled data exhibit a long-tailed distribution, and unlabeled data adhere to a typical predefined distribution (i.…
cs.LG2025
A Square Peg in a Square Hole: Meta-Expert for Long-Tailed Semi-Supervised Learning
Yaxin Hou, Yuheng Jia
This paper studies the long-tailed semi-supervised learning (LTSSL) with distribution mismatch, where the class distribution of the labeled training data follows a long-tailed dist…