Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Convolutional Neural Networks on Randomized Data   Convolutional Neural Networks (CNNs) are build specifically for computer
vision tasks for which it is known that the input data is a hierarchical
structure based on locally correlated elements. The question that naturally
arises is what happens with the performance of CNNs if one of the basic
properties of the data is removed, e.g. what happens if the image pixels are
randomly permuted? Intuitively one expects that the convolutional network
performs poorly in these circumstances in contrast to a multilayer perceptron
(MLPs) whose classification accuracy should not be affected by the pixel
randomization. This work shows that by randomizing image pixels the
hierarchical structure of the data is destroyed and long range correlations are
introduced which standard CNNs are not able to capture. We show that their
classification accuracy is heavily dependent on the class similarities as well
as the pixel randomization process. We also indicate that dilated convolutions
are able to recover some of the pixel correlations and improve the performance.

21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph