# ArtGAN: Artwork Synthesis with Conditional Categorical GANs

arXiv: https://arxiv.org/abs/1702.03410 · HTML: https://mlsanity.com/paper/1702.03410/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2017.

## Built on by (most cited first, 17 of 17)

- [The ArtBench Dataset: Benchmarking Generative Models with Artworks](https://mlsanity.com/paper/2206.11404/) (2022)
- [Draw Your Art Dream: Diverse Digital Art Synthesis with Multimodal Guided Diffusion](https://mlsanity.com/paper/2209.13360/) (2022)
- [Creativity and Machine Learning: A Survey](https://mlsanity.com/paper/2104.02726/) (2021)
- [Painterly Image Harmonization in Dual Domains](https://mlsanity.com/paper/2212.08846/) (2022)
- [Rethinking Artistic Copyright Infringements in the Era of Text-to-Image Generative Models](https://mlsanity.com/paper/2404.08030/) (2024)
- [Creative Painting with Latent Diffusion Models](https://mlsanity.com/paper/2209.14697/) (2022)
- [Stylebreeder: Exploring and Democratizing Artistic Styles through Text-to-Image Models](https://mlsanity.com/paper/2406.14599/) (2024)
- [From paintbrush to pixel: A review of deep neural networks in AI-generated art](https://mlsanity.com/paper/2302.10913/) (2023)
- [Harnessing the Conditioning Sensorium for Improved Image Translation](https://mlsanity.com/paper/2110.06443/) (2021)
- [Controllable Multi-domain Semantic Artwork Synthesis](https://mlsanity.com/paper/2308.10111/) (2023)
- [Continuation of Famous Art with AI: A Conditional Adversarial Network Inpainting Approach](https://mlsanity.com/paper/2110.09170/) (2021)
- [PhotoGAN: Generative Adversarial Neural Network Acceleration with Silicon Photonics](https://mlsanity.com/paper/2501.13828/) (2025)
- [Regularized Generative Adversarial Network](https://mlsanity.com/paper/2102.04593/) (2021)
- [Independent Encoder for Deep Hierarchical Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/2107.02494/) (2021)
- [Painterly Image Harmonization via Adversarial Residual Learning](https://mlsanity.com/paper/2311.08646/) (2023)
- [A Tiered GAN Approach for Monet-Style Image Generation](https://mlsanity.com/paper/2412.05724/) (2024)
- [Unified Kernel-Segregated Transpose Convolution Operation](https://mlsanity.com/paper/2502.20493/) (2025)

## Abstract (arXiv)

This paper proposes an extension to the Generative Adversarial Networks (GANs), namely as ARTGAN to synthetically generate more challenging and complex images such as artwork that have abstract characteristics. This is in contrast to most of the current solutions that focused on generating natural images such as room interiors, birds, flowers and faces. The key innovation of our work is to allow back-propagation of the loss function w.r.t. the labels (randomly assigned to each generated images) to the generator from the discriminator. With the feedback from the label information, the generator is able to learn faster and achieve better generated image quality. Empirically, we show that the proposed ARTGAN is capable to create realistic artwork, as well as generate compelling real world images that globally look natural with clear shape on CIFAR-10.