Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Understanding the Effective Receptive Field in Deep Convolutional Neural
  Networks   We study characteristics of receptive fields of units in deep convolutional
networks. The receptive field size is a crucial issue in many visual tasks, as
the output must respond to large enough areas in the image to capture
information about large objects. We introduce the notion of an effective
receptive field, and show that it both has a Gaussian distribution and only
occupies a fraction of the full theoretical receptive field. We analyze the
effective receptive field in several architecture designs, and the effect of
nonlinear activations, dropout, sub-sampling and skip connections on it. This
leads to suggestions for ways to address its tendency to be too small.

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The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ