# SP-BatikGAN: An Efficient Generative Adversarial Network for Symmetric Pattern Generation

arXiv: https://arxiv.org/abs/2304.09384 · HTML: https://mlsanity.com/paper/2304.09384/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 19 Apr 2023.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [Improved Precision and Recall Metric for Assessing Generative Models](https://mlsanity.com/paper/1904.06991/) (2019)
- [Reliable Fidelity and Diversity Metrics for Generative Models](https://mlsanity.com/paper/2002.09797/) (2020)
- [Efficient Attention: Attention with Linear Complexities](https://mlsanity.com/paper/1812.01243/) (2018)
- [Differentiable Augmentation for Data-Efficient GAN Training](https://mlsanity.com/paper/2006.10738/) (2020)
- [Exploring Self-attention for Image Recognition](https://mlsanity.com/paper/2004.13621/) (2020)
- [Are GANs Created Equal? A Large-Scale Study](https://mlsanity.com/paper/1711.10337/) (2017)
- [Geometric GAN](https://mlsanity.com/paper/1705.02894/) (2017)
- [Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis](https://mlsanity.com/paper/2101.04775/) (2021)
- [Dual Contrastive Loss and Attention for GANs](https://mlsanity.com/paper/2103.16748/) (2021)

## Built on by (most cited first, 1 of 1)

- [Synthesis of Batik Motifs using a Diffusion -- Generative Adversarial Network](https://mlsanity.com/paper/2307.12122/) (2023)

## Abstract (arXiv)

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.