Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Batch Normalization with Enhanced Linear Transformation   Batch normalization (BN) is a fundamental unit in modern deep networks, in
which a linear transformation module was designed for improving BN's
flexibility of fitting complex data distributions. In this paper, we
demonstrate properly enhancing this linear transformation module can
effectively improve the ability of BN. Specifically, rather than using a single
neuron, we propose to additionally consider each neuron's neighborhood for
calculating the outputs of the linear transformation. Our method, named BNET,
can be implemented with 2-3 lines of code in most deep learning libraries.
Despite the simplicity, BNET brings consistent performance gains over a wide
range of backbones and visual benchmarks. Moreover, we verify that BNET
accelerates the convergence of network training and enhances spatial
information by assigning the important neurons with larger weights accordingly.
The code is available at https://github.com/yuhuixu1993/BNET.

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The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
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The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
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The categories of this paper are: hep-ex
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The categories of this paper are: funct-an
2
The categories of this paper are: math
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The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
19
The categories of this paper are: eess
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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol