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PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning.

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    This study introduces PiCO+, a novel framework for partial label learning (PLL) that effectively handles noisy labels by disambiguating candidate label sets and mitigating noise. PiCO+ significantly improves performance on both standard and noisy PLL tasks, even matching fully supervised learning outcomes.

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    Area of Science:

    • Machine Learning
    • Computer Science

    Background:

    • Partial Label Learning (PLL) assigns a set of candidate labels to each training instance, assuming the true label is within this set.
    • A significant challenge arises when annotators provide incorrect candidate sets, leading to the noisy PLL problem.

    Purpose of the Study:

    • To propose the PiCO+ framework for simultaneously disambiguating candidate label sets and mitigating label noise in PLL.
    • To enhance the robustness of PLL methods against label noise and out-of-distribution data.

    Main Methods:

    • Developed the PiCO algorithm, incorporating contrastive learning and class prototype-based disambiguation, theoretically justified by an Expectation-Maximization (EM) algorithm.
    • Extended PiCO to PiCO+ by adding distance-based clean sample selection and semi-supervised contrastive learning for robust classifier training.
    • Investigated PiCO+'s robustness to out-of-distribution noise using a novel energy-based rejection method.

    Main Results:

    • PiCO+ significantly outperforms existing state-of-the-art methods on both standard and noisy partial label learning tasks.
    • The proposed methods achieve performance comparable to fully supervised learning in extensive experiments.
    • The framework demonstrates enhanced robustness against various types of label noise and out-of-distribution data.

    Conclusions:

    • The PiCO+ framework offers a robust and effective solution for the challenging noisy partial label learning problem.
    • The combination of label disambiguation and noise mitigation techniques leads to superior performance.
    • PiCO+ represents a significant advancement in partial label learning, broadening its practical applicability.