Geometric Transformations Embedded into Convolutional Neural Networks
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Keywords

artificial intelligence
machine learning
deep learning
convolutional neural networks
image processing
image recognition
geometric transformations

How to Cite

Tarasiuk, P., & Pryczek, M. (2016). Geometric Transformations Embedded into Convolutional Neural Networks. Journal of Applied Computer Science, 24(3), 33-48. https://doi.org/10.34658/jacs.2016.24.3.33-48

Abstract

This paper presents a novel extension to convolutional neural networks. While CNNs are known for invariance to object translation, changes to the other parameters could make the image recognition tasks diffcult – that includes rotations and scaling. Some improvement in this area could be achieved with embedded geometric transformations used inside the CNNs. In order to provide a practical solution, which allows fast propagation and learning of the modified networks, “fast geometric transformations” are introduced.

https://doi.org/10.34658/jacs.2016.24.3.33-48
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