Are CNNs that Learn to Predict Image Statistics Invariant to Domain Shifts?

Bachelor thesis (2021)

Authors

J.P. Biesheuvel Electrical Engineering, Mathematics and Computer Science

Contributors

T.J. Viering Electrical Engineering, Mathematics and Computer Science (mentor)
Z. Wang Pattern Recognition and Bioinformatics - EEMCS (mentor)
D.M.J. Tax Pattern Recognition and Bioinformatics - EEMCS (mentor)
M. Loog Pattern Recognition and Bioinformatics - EEMCS (mentor)
K.A. Hildebrandt Computer Graphics and Visualisation - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2021 Julian Biesheuvel
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Published Date

02-07-2021

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Yes, convolutional neural networks are domain-invariant, albeit to some limited extent. We explored the performance impact of domain shift for convolutional neural networks. We did this by designing new synthetic tasks, for which the network’s task was to map images to their mean, median, standard deviation, and variance pixel intensities. We find that the performance drop due to domain shift is related to the shift in pixel values between source and target domain. Colour space transformations seemed to notably impact the network’s performance, opposed to geometric transformations. For the last domain shift we find that the network manages to beat a baseline, from which we can conclude the domain shift is not too severe. Additionally, the findings reveal a less dominant role for feature transferability, for our synthetic regression tasks.