Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Transitioning between Convolutional and Fully Connected Layers in Neural
  Networks   Digital pathology has advanced substantially over the last decade however
tumor localization continues to be a challenging problem due to highly complex
patterns and textures in the underlying tissue bed. The use of convolutional
neural networks (CNNs) to analyze such complex images has been well adopted in
digital pathology. However in recent years, the architecture of CNNs have
altered with the introduction of inception modules which have shown great
promise for classification tasks. In this paper, we propose a modified
"transition" module which learns global average pooling layers from filters of
varying sizes to encourage class-specific filters at multiple spatial
resolutions. We demonstrate the performance of the transition module in AlexNet
and ZFNet, for classifying breast tumors in two independent datasets of scanned
histology sections, of which the transition module was superior.

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The categories of this paper are: comp-gas
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The categories of this paper are: eess
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
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The categories of this paper are: hep-th
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The categories of this paper are: adap-org
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The categories of this paper are: econ
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The categories of this paper are: q-fin
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The categories of this paper are: q-alg
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The categories of this paper are: hep-lat
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The categories of this paper are: cmp-lg
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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: plasm-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga