Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: InterActive: Inter-Layer Activeness Propagation   An increasing number of computer vision tasks can be tackled with deep
features, which are the intermediate outputs of a pre-trained Convolutional
Neural Network. Despite the astonishing performance, deep features extracted
from low-level neurons are still below satisfaction, arguably because they
cannot access the spatial context contained in the higher layers. In this
paper, we present InterActive, a novel algorithm which computes the activeness
of neurons and network connections. Activeness is propagated through a neural
network in a top-down manner, carrying high-level context and improving the
descriptive power of low-level and mid-level neurons. Visualization indicates
that neuron activeness can be interpreted as spatial-weighted neuron responses.
We achieve state-of-the-art classification performance on a wide range of image
datasets.

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The categories of this paper are: hep-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
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The categories of this paper are: solv-int
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The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
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The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: q-bio
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The categories of this paper are: hep-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol
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The categories of this paper are: acc-phys