# DeepCreativity: Measuring Creativity with Deep Learning Techniques

arXiv: https://arxiv.org/abs/2201.06118 · HTML: https://mlsanity.com/paper/2201.06118/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 58 references that are in the map.
- First posted on arXiv 16 Jan 2022.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Conditional Image Generation with PixelCNN Decoders](https://mlsanity.com/paper/1606.05328/) (2016)
- [Pixel Recurrent Neural Networks](https://mlsanity.com/paper/1601.06759/) (2016)
- [Image Transformer](https://mlsanity.com/paper/1802.05751/) (2018)
- [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://mlsanity.com/paper/1609.05473/) (2016)
- [A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications](https://mlsanity.com/paper/2001.06937/) (2020)
- [Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers](https://mlsanity.com/paper/2007.01547/) (2020)
- [CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms](https://mlsanity.com/paper/1706.07068/) (2017)
- [MaskGAN: Better Text Generation via Filling in the______](https://mlsanity.com/paper/1801.07736/) (2018)
- [The Optimal Reward Baseline for Gradient-Based Reinforcement Learning](https://mlsanity.com/paper/1301.2315/) (2013)
- [How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary?](https://mlsanity.com/paper/1511.05101/) (2015)
- [Creativity and Machine Learning: A Survey](https://mlsanity.com/paper/2104.02726/) (2021)
- [Quantifying Creativity in Art Networks](https://mlsanity.com/paper/1506.00711/) (2015)

## Built on by (most cited first, 7 of 7)

- [Can Copyright be Reduced to Privacy?](https://mlsanity.com/paper/2305.14822/) (2023)
- [Evaluating Large Language Model Creativity from a Literary Perspective](https://mlsanity.com/paper/2312.03746/) (2023)
- [Tackling Copyright Issues in AI Image Generation Through Originality Estimation and Genericization](https://mlsanity.com/paper/2406.03341/) (2024)
- [Challenges in creative generative models for music: a divergence maximization perspective](https://mlsanity.com/paper/2211.08856/) (2022)
- [Creativity in AI as Emergence from Domain-Limited Generative Models](https://mlsanity.com/paper/2601.08388/) (2026)
- [CresOWLve: Benchmarking Creative Problem-Solving Over Real-World Knowledge](https://mlsanity.com/paper/2604.03374/) (2026)
- [Towards Defining, Measuring, and Leveraging Creativity in AI Applications](https://mlsanity.com/paper/2610.12845/) (2026)

## Abstract (arXiv)

Measuring machine creativity is one of the most fascinating challenges in Artificial Intelligence. This paper explores the possibility of using generative learning techniques for automatic assessment of creativity. The proposed solution does not involve human judgement, it is modular and of general applicability. We introduce a new measure, namely DeepCreativity, based on Margaret Boden's definition of creativity as composed by value, novelty and surprise. We evaluate our methodology (and related measure) considering a case study, i.e., the generation of 19th century American poetry, showing its effectiveness and expressiveness.