Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: What Else Can Fool Deep Learning? Addressing Color Constancy Errors on
  Deep Neural Network Performance   There is active research targeting local image manipulations that can fool
deep neural networks (DNNs) into producing incorrect results. This paper
examines a type of global image manipulation that can produce similar adverse
effects. Specifically, we explore how strong color casts caused by incorrectly
applied computational color constancy - referred to as white balance (WB) in
photography - negatively impact the performance of DNNs targeting image
segmentation and classification. In addition, we discuss how existing image
augmentation methods used to improve the robustness of DNNs are not well suited
for modeling WB errors. To address this problem, a novel augmentation method is
proposed that can emulate accurate color constancy degradation. We also explore
pre-processing training and testing images with a recent WB correction
algorithm to reduce the effects of incorrectly white-balanced images. We
examine both augmentation and pre-processing strategies on different datasets
and demonstrate notable improvements on the CIFAR-10, CIFAR-100, and ADE20K
datasets.

31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con