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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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BNET: Batch Normalization With Enhanced Linear Transformation.

Yuhui Xu, Lingxi Xie, Cihang Xie

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    Batch normalization with enhanced linear transformation (BNET) improves deep learning models by aggregating neuron neighborhoods. This method enhances representation ability and accelerates training for various visual tasks.

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    Area of Science:

    • Deep Learning
    • Computer Vision

    Background:

    • Batch Normalization (BN) is crucial in deep neural networks.
    • Existing BN variants focus on normalization statistics, neglecting the recovery step's potential.

    Purpose of the Study:

    • To enhance the recovery step in Batch Normalization.
    • To improve the capacity of fitting complex data distributions.
    • To introduce a method that embeds spatial contextual information.

    Main Methods:

    • Propose Batch Normalization with Enhanced Linear Transformation (BNET).
    • Utilize depth-wise convolution for easy implementation.
    • Integrate BNET seamlessly into existing architectures with BN.

    Main Results:

    • BNET consistently improves performance across various backbones and visual tasks.
    • BNET accelerates network training convergence.
    • BNET enhances spatial information by assigning weights to important neurons.

    Conclusions:

    • BNET is the first method to enhance the recovery step of Batch Normalization.
    • BN is a special case of BNET from spatial and spectral perspectives.
    • BNET offers a simple yet effective way to boost deep learning model performance.