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    Evidential deep learning (EDL) models now offer uncertainty awareness. New activations and regularizers overcome learning-freeze issues, improving evidence updates for better AI model performance.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Deep Learning

    Background:

    • Evidential deep learning (EDL) models, grounded in Subjective Logic, enhance deterministic neural networks with uncertainty quantification.
    • Current EDL models face challenges due to non-negative evidence constraints, leading to activation-dependent learning-freeze behavior and diminished gradients in low-evidence regions.

    Purpose of the Study:

    • To theoretically analyze the activation-dependent learning-freeze behavior in EDL models.
    • To develop novel activation functions and regularizers for consistent evidence updates across different activation regimes.

    Main Methods:

    • Theoretical characterization of learning-freeze behavior in EDL models.
    • Design of a generalized family of activation functions and corresponding evidential regularizers.
    • Empirical validation on benchmark image classification, few-shot learning, and blind face restoration tasks.

    Main Results:

    • The study theoretically explains the learning-freeze phenomenon in EDL models.
    • The proposed generalized regularized evidential models demonstrate consistent evidence updates.
    • Experiments show the effectiveness of the new approach across diverse computer vision tasks.

    Conclusions:

    • The developed activation functions and regularizers provide a robust alternative for EDL models.
    • This work addresses key limitations, enabling more reliable uncertainty quantification in deep learning.
    • The findings pave the way for improved performance in AI systems requiring nuanced uncertainty estimation.