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Output Regularization With Cluster-Based Soft Targets.

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    This study introduces Cluster-based soft targets for Output Regularization (CluOReg) to improve neural network generalization in image classification. CluOReg effectively reduces classification error by integrating clustering with output regularization.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Over-parameterized neural networks excel in image classification but suffer from overfitting, leading to poor generalization.
    • Output regularization uses soft targets to mitigate overfitting, but existing methods overlook clustering's structural information.

    Purpose of the Study:

    • To propose Cluster-based soft targets for Output Regularization (CluOReg) for enhanced neural network generalization.
    • To integrate clustering with output regularization for simultaneous embedding space clustering and classifier training.

    Main Methods:

    • Developed CluOReg, a novel approach leveraging clustering for output regularization.
    • Calculated a class relationship matrix in cluster space to derive classwise soft targets.
    • Employed these soft targets to complement ground-truth labels during neural network training.

    Main Results:

    • Achieved consistent and significant reductions in image classification error across benchmark datasets.
    • Demonstrated effectiveness without external models or data augmentation.
    • Showcased CluOReg's ability to complement ground-truth labels effectively.

    Conclusions:

    • Cluster-based soft targets offer a powerful mechanism to enhance neural network generalization.
    • CluOReg provides a unified framework for integrating clustering and output regularization.
    • The proposed method effectively addresses overfitting in over-parameterized neural networks for image classification.