Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A concatenating framework of shortcut convolutional neural networks   It is well accepted that convolutional neural networks play an important role
in learning excellent features for image classification and recognition.
However, in tradition they only allow adjacent layers connected, limiting
integration of multi-scale information. To further improve their performance,
we present a concatenating framework of shortcut convolutional neural networks.
This framework can concatenate multi-scale features by shortcut connections to
the fully-connected layer that is directly fed to the output layer. We do a
large number of experiments to investigate performance of the shortcut
convolutional neural networks on many benchmark visual datasets for different
tasks. The datasets include AR, FERET, FaceScrub, CelebA for gender
classification, CUReT for texture classification, MNIST for digit recognition,
and CIFAR-10 for object recognition. Experimental results show that the
shortcut convolutional neural networks can achieve better results than the
traditional ones on these tasks, with more stability in different settings of
pooling schemes, activation functions, optimizations, initializations, kernel
numbers and kernel sizes.

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The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
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The categories of this paper are: eess
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The categories of this paper are: alg-geom
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The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
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The categories of this paper are: math-ph
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The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
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The categories of this paper are: chem-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: plasm-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: bayes-an