Self-Supervised Learning in Horticulture REDUCING THE NEED FOR ANNOTATED DATA IN NEURAL NETWORK TRAINING

ASSELDONK, Bram van

Self-Supervised Learning in Horticulture REDUCING THE NEED FOR ANNOTATED DATA IN NEURAL NETWORK TRAINING

Files

S4476883_AsseldonkB.pdf (20.49 MB)

Authors

ASSELDONK, Bram van

Issue Date

2020-06-01

Language

en

URI

https://theses.ubn.ru.nl/handle/123456789/10622

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.