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Related Experiment Videos

Local Semantics Refinement of Adaptive Representations for Robust Noisy Label Learning.

Yueer Lin, Yang Zhang, Jiao Hou

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |May 29, 2026
    PubMed
    Summary

    Deep learning models struggle with noisy labels. Our new framework, Local Semantics Refinement of Adaptive Representations (LFDA), improves label quality by refining representations and aligning features, outperforming existing methods.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computer Vision

    Background:

    • Deep learning models require high-quality labeled data, but large datasets often contain inevitable noisy labels.
    • Current methods for handling noisy labels may exhibit confirmation bias and neglect valuable hard-but-clean samples.

    Purpose of the Study:

    • To introduce a novel framework, Local Semantics Refinement of Adaptive Representations (LFDA), designed to adaptively refine label quality in deep learning datasets.
    • To address the limitations of existing label noise learning methods by leveraging local feature consistency and representation alignment.

    Main Methods:

    • LFDA utilizes a Local Consistency Score module to assess sample similarity in latent space, distinguishing clean from noisy labels.
    • A confidence neighborhood is constructed for local reference guidance, improving the identification and correction of noisy instances.

    Related Experiment Videos

  • The Reliability-Aware Representation Alignment (RRA) module aligns high-confidence representations to implicitly refine low-confidence instances via soft supervision.

    • Main Results:

    • LFDA consistently outperforms state-of-the-art methods on both synthetic and real-world noisy datasets.
    • Experimental results demonstrate LFDA's robustness and generalization ability across diverse noise conditions.
    • The framework effectively handles complex noise scenarios in label noise learning.

    Conclusions:

    • LFDA offers a robust and effective solution for learning with noisy labels in deep learning.
    • The proposed method enhances label quality by leveraging local semantic information and adaptive representation refinement.
    • LFDA represents a significant advancement in addressing the challenge of noisy labels in large-scale datasets.