4 papers
Beyond Distribution Estimation: Simplex Anchored Structural Inference Towards Universal Semi-Supervised Learning
Yaxin Hou, Jun Ma, Hanyang Li +3
Semi-supervised learning faces significant challenges in realistic scenarios where labeled data is scarce and unlabeled data follows unknown, arbitrary distributions. We formalize…
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.…
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…
Irregular Tensor Low-Rank Representation for Hyperspectral Image Representation
Bo Han, Yuheng Jia, Hui Liu +1
Spectral variations pose a common challenge in analyzing hyperspectral images (HSI). To address this, low-rank tensor representation has emerged as a robust strategy, leveraging in…