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Related Experiment Video

Updated: Jul 19, 2025

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
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Revisiting AUC-Oriented Adversarial Training With Loss-Agnostic Perturbations.

Zhiyong Yang, Qianqian Xu, Wenzheng Hou

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |August 10, 2023
    PubMed
    Summary

    This study enhances adversarial training for Area Under the ROC curve (AUC) classification. It introduces a new algorithm compatible with existing methods, ensuring robustness and generalization for long-tail datasets.

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    Area of Science:

    • Machine Learning
    • Computer Vision

    Background:

    • Area Under the ROC curve (AUC) is crucial for long-tail classification.
    • Existing AUC optimization methods lack robustness against adversarial attacks.
    • AdAUC offers AUC-oriented adversarial training but has limitations in real-world applicability.

    Purpose of the Study:

    • To develop a more practical and robust AUC-oriented adversarial training method.
    • To address the limitations of global perturbation generation in AdAUC.
    • To provide a theoretical guarantee for the generalization ability of AUC adversarial training.

    Main Methods:

    • Reformulated the original AUC adversarial training objective function.
    • Proposed an inducing algorithm compatible with score-based and instance-wise-loss-based perturbations.
    • Developed a fast SVRG-based gradient descent-ascent algorithm for acceleration.

    Main Results:

    • Demonstrated equivalence between AdAUC and score/instance-wise perturbations under mild conditions.
    • Established an explicit error bound for AUC-oriented adversarial training, ensuring generalization.
    • Achieved strong performance and robustness on five long-tail datasets through extensive experiments.

    Conclusions:

    • The proposed method enhances AUC adversarial training's compatibility and robustness.
    • Theoretical guarantees for generalization are established.
    • The algorithm offers significant improvements in performance and robustness for long-tail classification tasks.