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- W4366552808 abstract "Following the contention of AI arts, our research focuses on bringing AI for all, particularly for artists, to create AI arts with limited data and settings. We are interested in geometrically symmetric pattern generation, which appears on many artworks such as Portuguese, Moroccan tiles, and Batik, a cultural heritage in Southeast Asia. Symmetric pattern generation is a complex problem, with prior research creating too-specific models for certain patterns only. We provide publicly, the first-ever 1,216 high-quality symmetric patterns straight from design files for this task. We then formulate symmetric pattern enforcement (SPE) loss to leverage underlying symmetric-based structures that exist on current image distributions. Our SPE improves and accelerates training on any GAN configuration, and, with efficient attention, SP-BatikGAN compared to FastGAN, the state-of-the-art GAN for limited setting, improves the FID score from 110.11 to 90.76, an 18% decrease, and model diversity recall score from 0.047 to 0.204, a 334% increase." @default.
- W4366552808 created "2023-04-22" @default.
- W4366552808 creator A5010168452 @default.
- W4366552808 creator A5047228155 @default.
- W4366552808 date "2023-04-18" @default.
- W4366552808 modified "2023-09-27" @default.
- W4366552808 title "SP-BatikGAN: An Efficient Generative Adversarial Network for Symmetric Pattern Generation" @default.
- W4366552808 doi "https://doi.org/10.48550/arxiv.2304.09384" @default.
- W4366552808 hasPublicationYear "2023" @default.
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