# Creative divergent synthesis with generative models

arXiv: https://arxiv.org/abs/2211.08861 · HTML: https://mlsanity.com/paper/2211.08861/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 16 Nov 2022.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Assessing Generative Models via Precision and Recall](https://mlsanity.com/paper/1806.00035/) (2018)
- [A Note on the Inception Score](https://mlsanity.com/paper/1801.01973/) (2018)
- [Active Divergence with Generative Deep Learning -- A Survey and Taxonomy](https://mlsanity.com/paper/2107.05599/) (2021)
- [Creativity in the era of artificial intelligence](https://mlsanity.com/paper/2008.05959/) (2020)

## Abstract (arXiv)

Machine learning approaches now achieve impressive generation capabilities in numerous domains such as image, audio or video. However, most training & evaluation frameworks revolve around the idea of strictly modelling the original data distribution rather than trying to extrapolate from it. This precludes the ability of such models to diverge from the original distribution and, hence, exhibit some creative traits. In this paper, we propose various perspectives on how this complicated goal could ever be achieved, and provide preliminary results on our novel training objective called textitBounded Adversarial Divergence (BAD).