Abstract
Following recent advances on parameterized hypercomplex multiplication [21], we explore the usefulness of hypercomplex convolutions and deconvolutions in a document labeling task. We show that the proposed Hypercomplex Generative Adversarial Networks achieve excellent results while requiring significantly less independent parameters than real-valued models.
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Acknowledgments
This research has been partially co-financed by the EU and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the calls “OPEN INNOVATION IN CULTURE” (project Bessarion - T6YB\(\varPi \)-00214) and “RESEARCH - CREATE - INNOVATE” (project Culdile - T1E\(\varDelta \)K-03785).
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Sfikas, G., Retsinas, G., Gatos, B., Nikou, C. (2022). Hypercomplex Generative Adversarial Networks for Lightweight Semantic Labeling. In: El Yacoubi, M., Granger, E., Yuen, P.C., Pal, U., Vincent, N. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2022. Lecture Notes in Computer Science, vol 13363. Springer, Cham. https://doi.org/10.1007/978-3-031-09037-0_21
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