# Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities

arXiv: https://arxiv.org/abs/2107.01858 · HTML: https://mlsanity.com/paper/2107.01858/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 58 references that are in the map.
- First posted on arXiv 5 Jul 2021.

## Builds on (most cited first, 19 of 19)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [Rewriting a Deep Generative Model](https://mlsanity.com/paper/2007.15646/) (2020)
- [AutoGAN: Neural Architecture Search for Generative Adversarial Networks](https://mlsanity.com/paper/1908.03835/) (2019)
- [AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks](https://mlsanity.com/paper/2006.08198/) (2020)
- [Discriminator Rejection Sampling](https://mlsanity.com/paper/1810.06758/) (2018)
- [Evolutionary Generative Adversarial Networks](https://mlsanity.com/paper/1803.00657/) (2018)
- [AdversarialNAS: Adversarial Neural Architecture Search for GANs](https://mlsanity.com/paper/1912.02037/) (2019)
- [Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools](https://mlsanity.com/paper/1908.05557/) (2019)
- [Autonomy, Authenticity, Authorship and Intention in computer generated art](https://mlsanity.com/paper/1903.02166/) (2019)
- [COEGAN: Evaluating the Coevolution Effect in Generative Adversarial Networks](https://mlsanity.com/paper/1912.06180/) (2019)
- [Automated Machine Learning in Practice: State of the Art and Recent Results](https://mlsanity.com/paper/1907.08392/) (2019)
- [A Survey and Taxonomy of Adversarial Neural Networks for Text-to-Image Synthesis](https://mlsanity.com/paper/1910.09399/) (2019)
- [Amplifying The Uncanny](https://mlsanity.com/paper/2002.06890/) (2020)
- [Combinets: Creativity via Recombination of Neural Networks](https://mlsanity.com/paper/1802.03605/) (2018)

## Built on by (most cited first, 2 of 2)

- [Active Divergence with Generative Deep Learning -- A Survey and Taxonomy](https://mlsanity.com/paper/2107.05599/) (2021)
- [Towards Mode Balancing of Generative Models via Diversity Weights](https://mlsanity.com/paper/2304.11961/) (2023)

## Abstract (arXiv)

We present a framework for automating generative deep learning with a specific focus on artistic applications. The framework provides opportunities to hand over creative responsibilities to a generative system as targets for automation. For the definition of targets, we adopt core concepts from automated machine learning and an analysis of generative deep learning pipelines, both in standard and artistic settings. To motivate the framework, we argue that automation aligns well with the goal of increasing the creative responsibility of a generative system, a central theme in computational creativity research. We understand automation as the challenge of granting a generative system more creative autonomy, by framing the interaction between the user and the system as a co-creative process. The development of the framework is informed by our analysis of the relationship between automation and creative autonomy. An illustrative example shows how the framework can give inspiration and guidance in the process of handing over creative responsibility.