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Related Concept Videos

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Updated: Jul 11, 2025

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Instance-Dependent Inaccurate Label Distribution Learning.

Zhiqiang Kou, Jing Wang, Yuheng Jia

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    Label distribution learning (LDL) models often have noisy, inaccurate label distributions. This study introduces a new method to address instance-dependent noise in LDL, improving accuracy.

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    Area of Science:

    • Machine Learning
    • Computer Science

    Background:

    • Label distribution learning (LDL) assigns instances to label distributions.
    • Existing LDL algorithms struggle with inaccurate label distributions due to annotation noise.
    • Instance-dependent noise in label distributions has been overlooked.

    Purpose of the Study:

    • To identify and address the instance-dependent inaccurate LDL (IDI-LDL) problem.
    • To propose a novel algorithm, low-rank and sparse LDL (LRS-LDL), to tackle IDI-LDL.

    Main Methods:

    • Assuming inaccurate label distributions comprise ground-truth and instance-dependent noise.
    • Learning a low-rank mapping for ground-truth label distributions.
    • Learning a sparse mapping for instance-dependent noise.

    Main Results:

    • Theoretical generalization bound established for LRS-LDL.
    • Experimental validation demonstrating LRS-LDL's effectiveness.
    • LRS-LDL outperforms existing LDL methods in addressing IDI-LDL.

    Conclusions:

    • The proposed LRS-LDL algorithm effectively handles instance-dependent noise in label distributions.
    • LRS-LDL offers a significant improvement over traditional LDL methods.
    • This work advances the field of label distribution learning by addressing a critical noise issue.