Exploring the Learning of Face Selective Units in Deep Neural Networks
Keywords
No Thumbnail Available
Authors
Issue Date
2020-01-31
Language
en
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Because Arti cial Neural Networks are inspired by the brain, they can give
neuroscientists a powerful tool to understand what happens in human brains.
However, ANNs are often considered as black boxes. In this paper we have
tried to understand how deep neural networks compare in units that select
for faces when trained on various quantities of this category. The training of
four networks is based on a weight factor (0, 0.2, 0.5, 0.8) which re
ects the
in
uence of the occurrence of categories where one category consists of faces
(a high occurrence) and the other of sieves, shredders and hovercrafts (low
occurrence). This paper shows that the number of units that are signi cantly
active for faces stays the same across these networks, but that the accuracy of
category face becomes better with a higher weight factor. The accuracy for the
notface category becomes worse with a higher weight factor. Furthermore, it is
shown that in the training phase, the face category performs very well quickly
with a high weight factor, but the performance for the notface category gets
worse for a higher weight factor. Also, a relationship is shown between the
number of face active units and the feature maps and that very early in the
training phase a structure of the units that select signi cantly for faces becomes
visible.
Description
Citation
Supervisor
Faculty
Faculteit der Sociale Wetenschappen