Self-Supervised Learning in Horticulture REDUCING THE NEED FOR ANNOTATED DATA IN NEURAL NETWORK TRAINING
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2020-06-01
Language
en
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Abstract
While many opportunities for the use of technology driven by artificial neural networks exist in
horticulture, the adoption rate of such technology remains low in the domain. Part of this is due
to the large amount of time and effort required to gather annotated data for the training of neural
networks. In this thesis we explore self-supervised learning as a means to reduce the amount
of annotated data required for training in an attempt to make neural networks more accessible.
Specifically, we test whether pre-training a neural network on an image rotation estimation task
can benefit the network when it is trained on a different task afterwards. The performance of
the network is compared to the performance of a network without pre-training and a network
pre-trained on data from a publicly available dataset. We find that the combination of the data
and the pre-training task used in this thesis negatively affects any further training on a different
task. Salience maps reveal that this is due to the network learning to pay attention to pixels in
the background rather than pixels in the foreground during pre-training.
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Faculteit der Sociale Wetenschappen