Invariant Convolutional Networks

0576905 - ÚTIA 2024 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
Lébl, Matěj - Flusser, Jan
Invariant Convolutional Networks.
Proceedings of The 12th International Conference on Image Processing Theory, Tools and Applications (IPTA 2023). Piscataway: IEEE, 2023, č. článku 10319998. ISBN 979-8-3503-2541-6.
[International Conference on Image Processing Theory, Tools and Applications (IPTA 2023) /12./. Paris (FR), 16.10.2023-19.10.2023]
Grant CEP: GA ČR GA21-03921S
Grant ostatní: AV ČR(CZ) StrategieAV21/1
Program: StrategieAV
Institucionální podpora: RVO:67985556
Klíčová slova: Neural network * augmentation * blur
Obor OECD: Robotics and automatic control
http://library.utia.cas.cz/separaty/2023/ZOI/flusser-0576905.pdf

Neural networks are often trained on datasets, that are not fully representative of the expected query images. Many times, the difference stem from the query images being taken in sub-optimal conditions. The most common defects are rotation, scale, blur, noise and intensity & contrast change which were all thoroughly studied and described. In this paper we propose a novel neural network architecture which is invariant to such degradations by design. We incorporate the knowledge build for classical methods directly into the network architecture providing an alternative to the augmentation of the training dataset. In the experiments, the proposed solution outperforms the classical augmentation technique in both accuracy and computational resources needed.

Trvalý link: https://hdl.handle.net/11104/0346495