Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hierarchical Auxiliary Learning   Conventional application of convolutional neural networks (CNNs) for image
classification and recognition is based on the assumption that all target
classes are equal(i.e., no hierarchy) and exclusive of one another (i.e., no
overlap). CNN-based image classifiers built on this assumption, therefore,
cannot take into account an innate hierarchy among target classes (e.g., cats
and dogs in animal image classification) or additional information that can be
easily derived from the data (e.g.,numbers larger than five in the recognition
of handwritten digits), thereby resulting in scalability issues when the number
of target classes is large. Combining two related but slightly different ideas
of hierarchical classification and logical learning by auxiliary inputs, we
propose a new learning framework called hierarchical auxiliary learning, which
not only address the scalability issues with a large number of classes but also
could further reduce the classification/recognition errors with a reasonable
number of classes. In the hierarchical auxiliary learning, target classes are
semantically or non-semantically grouped into superclasses, which turns the
original problem of mapping between an image and its target class into a new
problem of mapping between a pair of an image and its superclass and the target
class. To take the advantage of superclasses, we introduce an auxiliary block
into a neural network, which generates auxiliary scores used as additional
information for final classification/recognition; in this paper, we add the
auxiliary block between the last residual block and the fully-connected output
layer of the ResNet. Experimental results demonstrate that the proposed
hierarchical auxiliary learning can reduce classification errors up to 0.56,
1.6 and 3.56 percent with MNIST, SVHN and CIFAR-10 datasets, respectively.

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The categories of this paper are: patt-sol
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The categories of this paper are: nlin
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The categories of this paper are: mtrl-th
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The categories of this paper are: funct-an
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The categories of this paper are: q-bio
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-lat
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: gr-qc
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The categories of this paper are: math
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The categories of this paper are: ao-sci
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The categories of this paper are: eess
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-th