Abstract
In this work, we introduce and discuss Quaternion Generative Adversarial Networks, a variant of generative adversarial networks that uses quaternion-valued inputs, weights and intermediate network representations. Quaternionic representation has the advantage of treating cross-channel information carried by multichannel signals (e.g. color images) holistically, while quaternionic convolution has been shown to be less resource-demanding. Standard convolutional and deconvolutional layers are replaced by their quaternionic variants, in both generator and discriminator nets, while activations and loss functions are adapted accordingly. We have succesfully tested the model on the task of detecting byzantine inscriptions in the wild, where the proposed model is on par with a vanilla conditional generative adversarial network, but is significantly less expensive in terms of model size (requires \(4{\times }\) less parameters). Code is available at https://github.com/sfikas/quaternion-gan.
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Acknowledgement
We would like to thank Dr. Christos Stavrakos, Dr. Katerina Kontopanagou, Dr. Fanny Lyttari and Ioannis Theodorakopoulos for supplying us with the Byzantine inscription images used for our experiments.
We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan XP GPU used for this research.
This research has been partially co-financed by the EU and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call OPEN INNOVATION IN CULTURE, project Bessarion (T6YB\(\varPi \)-00214).
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Sfikas, G., Giotis, A.P., Retsinas, G., Nikou, C. (2021). Quaternion Generative Adversarial Networks for Inscription Detection in Byzantine Monuments. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12667. Springer, Cham. https://doi.org/10.1007/978-3-030-68787-8_12
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