Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Synthesizing Visual Illusions Using Generative Adversarial Networks   Visual illusions are a very useful tool for vision scientists, because they
allow them to better probe the limits, thresholds and errors of the visual
system. In this work we introduce the first ever framework to generate novel
visual illusions with an artificial neural network (ANN). It takes the form of
a generative adversarial network, with a generator of visual illusion
candidates and two discriminator modules, one for the inducer background and
another that decides whether or not the candidate is indeed an illusion. The
generality of the model is exemplified by synthesizing illusions of different
types, and validated with psychophysical experiments that corroborate that the
outputs of our ANN are indeed visual illusions to human observers. Apart from
synthesizing new visual illusions, which may help vision researchers, the
proposed model has the potential to open new ways to study the similarities and
differences between ANN and human visual perception.

12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph