6 papers · 1 filter
PGSB: Pretrained-Guided Shared Basis for LoRA Model Merging
Muqing Liu, Chongjie Si, Zhuoya Liu +1
Large pretrained models have achieved remarkable success across diverse domains, yet adapting them to multiple downstream tasks remains challenging: full fine-tuning is costly, whi…
FlexLoRA: Entropy-Guided Flexible Low-Rank Adaptation
Muqing Liu, Chongjie Si, Yuheng Jia
Large pre-trained models achieve remarkable success across diverse domains, yet fully fine-tuning incurs prohibitive computational and memory costs. Parameter-efficient fine-tuning…
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…
ESMC: MLLM-Based Embedding Selection for Explainable Multiple Clustering
Xinyue Wang, Yuheng Jia, Hui Liu +1
Typical deep clustering methods, while achieving notable progress, can only provide one clustering result per dataset. This limitation arises from their assumption of a fixed under…
Towards Better IncomLDL: We Are Unaware of Hidden Labels in Advance
Jiecheng Jiang, Jiawei Tang, Jiahao Jiang +3
Label distribution learning (LDL) is a novel paradigm that describe the samples by label distribution of a sample. However, acquiring LDL dataset is costly and time-consuming, whic…
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…