1 citations · 1 across the 8 of their papers we have counts for
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Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity
Junxiang Wu, Zhiqiang Kou, Hongwei Zeng +7
Label Distribution Learning (LDL) models supervision as an instance-wise probability distribution, enabling fine-grained learning under inherent ambiguity, but its success relies o…
FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning
Zhiqiang Kou, Junxiang Wu, Wenke Huang +8
Federated Multi-Label Learning is a distributed paradigm where multiple clients possess heterogeneous multi-label data and perform collaborative learning under privacy constraints…
Positive-Unlabeled Reinforcement Learning Distillation for On-Premise Small Models
Zhiqiang Kou, Junyang Chen, Xin-Qiang Cai +8
Due to constraints on privacy, cost, and latency, on-premise deployment of small models is increasingly common. However, most practical pipelines stop at supervised fine-tuning (SF…
Concentration Distribution Learning from Label Distributions
Jiawei Tang, Yuheng Jia
Label distribution learning (LDL) is an effective method to predict the relative label description degree (a.k.a. label distribution) of a sample. However, the label distribution i…
Label Distribution Learning with Biased Annotations by Learning Multi-Label Representation
Zhiqiang Kou, Si Qin, Hailin Wang +6
Multi-label learning (MLL) has gained attention for its ability to represent real-world data. Label Distribution Learning (LDL), an extension of MLL to learning from label distribu…
Towards Better Performance in Incomplete LDL: Addressing Data Imbalance
Zhiqiang Kou, Haoyuan Xuan, Jing Wang +2
Label Distribution Learning (LDL) is a novel machine learning paradigm that addresses the problem of label ambiguity and has found widespread applications. Obtaining complete label…
Inaccurate Label Distribution Learning with Dependency Noise
Zhiqiang Kou, Jing Wang, Yuheng Jia +1
In this paper, we introduce the Dependent Noise-based Inaccurate Label Distribution Learning (DN-ILDL) framework to tackle the challenges posed by noise in label distribution learn…
RankMatch: A Novel Approach to Semi-Supervised Label Distribution Learning Leveraging Inter-label Correlations
Kouzhiqiang Yucheng Xie, Jing Wang, Yuheng Jia +2
This paper introduces RankMatch, an innovative approach for Semi-Supervised Label Distribution Learning (SSLDL). Addressing the challenge of limited labeled data, RankMatch effecti…