# GAN Computers Generate Arts? A Survey on Visual Arts, Music, and Literary Text Generation using Generative Adversarial Network

arXiv: https://arxiv.org/abs/2108.03857 · HTML: https://mlsanity.com/paper/2108.03857/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 58 references that are in the map.
- First posted on arXiv 9 Aug 2021.

## Builds on (most cited first, 29 of 29)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://mlsanity.com/paper/1609.05473/) (2016)
- [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks](https://mlsanity.com/paper/1506.05751/) (2015)
- [Regularizing and Optimizing LSTM Language Models](https://mlsanity.com/paper/1708.02182/) (2017)
- [MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment](https://mlsanity.com/paper/1709.06298/) (2017)
- [C-RNN-GAN: Continuous recurrent neural networks with adversarial training](https://mlsanity.com/paper/1611.09904/) (2016)
- [CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms](https://mlsanity.com/paper/1706.07068/) (2017)
- [MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation](https://mlsanity.com/paper/1703.10847/) (2017)
- [GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images](https://mlsanity.com/paper/2003.02567/) (2020)
- [Deep Learning for Free-Hand Sketch: A Survey](https://mlsanity.com/paper/2001.02600/) (2020)
- [Adversarial Text-to-Image Synthesis: A Review](https://mlsanity.com/paper/2101.09983/) (2021)
- [Adversarial Ranking for Language Generation](https://mlsanity.com/paper/1705.11001/) (2017)
- [Maximum-Likelihood Augmented Discrete Generative Adversarial Networks](https://mlsanity.com/paper/1702.07983/) (2017)
- [Adversarial Generation of Natural Language](https://mlsanity.com/paper/1705.10929/) (2017)
- [Conditional LSTM-GAN for Melody Generation from Lyrics](https://mlsanity.com/paper/1908.05551/) (2019)
- [Neural Painters: A learned differentiable constraint for generating brushstroke paintings](https://mlsanity.com/paper/1904.08410/) (2019)
- [KaoKore: A Pre-modern Japanese Art Facial Expression Dataset](https://mlsanity.com/paper/2002.08595/) (2020)
- [Beyond Narrative Description: Generating Poetry from Images by Multi-Adversarial Training](https://mlsanity.com/paper/1804.08473/) (2018)
- [Sketch-to-Art: Synthesizing Stylized Art Images From Sketches](https://mlsanity.com/paper/2002.12888/) (2020)
- [Generating images with recurrent adversarial networks](https://mlsanity.com/paper/1602.05110/) (2016)
- [Auto-painter: Cartoon Image Generation from Sketch by Using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1705.01908/) (2017)
- [The Six Fronts of the Generative Adversarial Networks](https://mlsanity.com/paper/1910.13076/) (2019)
- [Prose for a Painting](https://mlsanity.com/paper/1910.03634/) (2019)

## Built on by (most cited first, 15 of 15)

- [TAVGBench: Benchmarking Text to Audible-Video Generation](https://mlsanity.com/paper/2404.14381/) (2024)
- [Putting GPT-4o to the Sword: A Comprehensive Evaluation of Language, Vision, Speech, and Multimodal Proficiency](https://mlsanity.com/paper/2407.09519/) (2024)
- [An overview of domain-specific foundation model: key technologies, applications and challenges](https://mlsanity.com/paper/2409.04267/) (2024)
- [Diffusion-Based Visual Art Creation: A Survey and New Perspectives](https://mlsanity.com/paper/2408.12128/) (2024)
- [The Imitation Game: Detecting Human and AI-Generated Texts in the Era of ChatGPT and BARD](https://mlsanity.com/paper/2307.12166/) (2023)
- [GANs Conditioning Methods: A Survey](https://mlsanity.com/paper/2408.15640/) (2024)
- [TPPoet: Transformer-Based Persian Poem Generation using Minimal Data and Advanced Decoding Techniques](https://mlsanity.com/paper/2312.02125/) (2023)
- [Performance of diverse evaluation metrics in NLP-based assessment and text generation of consumer complaints](https://mlsanity.com/paper/2506.21623/) (2025)
- [A Comprehensive Survey of Natural Language Generation Advances from the Perspective of Digital Deception](https://mlsanity.com/paper/2208.05757/) (2022)
- [MITS-GAN: Safeguarding Medical Imaging from Tampering with Generative Adversarial Networks](https://mlsanity.com/paper/2401.09624/) (2024)
- [Level of agreement between emotions generated by Artificial Intelligence and human evaluation: a methodological proposal](https://mlsanity.com/paper/2410.08332/) (2024)
- [FlexMUSE: Multimodal Unification and Semantics Enhancement Framework with Flexible interaction for Creative Writing](https://mlsanity.com/paper/2508.16230/) (2025)
- [NFTGAN: Non-Fungible Token Art Generation Using Generative Adversarial Networks](https://mlsanity.com/paper/2112.10577/) (2021)
- [Machine Apophenia: The Kaleidoscopic Generation of Architectural Images](https://mlsanity.com/paper/2407.09172/) (2024)
- [Generative diffusion modeling protocols for improving the Kikuchi pattern indexing in electron back-scatter diffraction](https://mlsanity.com/paper/2510.26907/) (2025)

## Abstract (arXiv)

"Art is the lie that enables us to realize the truth." - Pablo Picasso. For centuries, humans have dedicated themselves to producing arts to convey their imagination. The advancement in technology and deep learning in particular, has caught the attention of many researchers trying to investigate whether art generation is possible by computers and algorithms. Using generative adversarial networks (GANs), applications such as synthesizing photorealistic human faces and creating captions automatically from images were realized. This survey takes a comprehensive look at the recent works using GANs for generating visual arts, music, and literary text. A performance comparison and description of the various GAN architecture are also presented. Finally, some of the key challenges in art generation using GANs are highlighted along with recommendations for future work.