# Unrolled Creative Adversarial Network For Generating Novel Musical Pieces

arXiv: https://arxiv.org/abs/2501.00452 · HTML: https://mlsanity.com/paper/2501.00452/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 13 references that are in the map.
- First posted on arXiv 31 Dec 2024.

## Builds on (most cited first, 9 of 9)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Expert Gate: Lifelong Learning with a Network of Experts](https://mlsanity.com/paper/1611.06194/) (2016)
- [Unrolled Generative Adversarial Networks](https://mlsanity.com/paper/1611.02163/) (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)
- [Music Generation by Deep Learning - Challenges and Directions](https://mlsanity.com/paper/1712.04371/) (2017)
- [DeSIGN: Design Inspiration from Generative Networks](https://mlsanity.com/paper/1804.00921/) (2018)
- [Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions](https://mlsanity.com/paper/2002.00212/) (2020)
- [Generating Music from Literature](https://mlsanity.com/paper/1403.2124/) (2014)

## Abstract (arXiv)

Music generation has emerged as a significant topic in artificial intelligence and machine learning. While recurrent neural networks (RNNs) have been widely employed for sequence generation, generative adversarial networks (GANs) remain relatively underexplored in this domain. This paper presents two systems based on adversarial networks for music generation. The first system learns a set of music pieces without differentiating between styles, while the second system focuses on learning and deviating from specific composers' styles to create innovative music. By extending the Creative Adversarial Networks (CAN) framework to the music domain, this work introduces unrolled CAN to address mode collapse, evaluating both GAN and CAN in terms of creativity and variation.