mlsanityData to 7 Oct 2026

DeepCreativity: Measuring Creativity with Deep Learning Techniques

arXiv 2201.06118 ↗First posted 16 Jan 2022cs.LG cs.AI cs.CYcited by 6

Not on the map. Why?

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 16 of its 58 references that are in the map. First posted on arXiv 16 Jan 2022.

Builds on

16 of 58 references.

References · 16

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  3. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  4. 2014Convolutional Neural Networks for Sentence Classificationcited by 1,124
  5. 2016Conditional Image Generation with PixelCNN Decoderscited by 859
  6. 2016Pixel Recurrent Neural Networkscited by 814
  7. 2018Image Transformercited by 770
  8. 2016SeqGAN: Sequence Generative Adversarial Nets with Policy Gradientcited by 375
  9. 2020A Review on Generative Adversarial Networks: Algorithms, Theory, and Applicationscited by 209
  10. 2020Descending through a Crowded Valley - Benchmarking Deep Learning Optimizerscited by 118
  11. 2017CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Normscited by 96
  12. 2018MaskGAN: Better Text Generation via Filling in the______cited by 89
4 more
  1. 2013The Optimal Reward Baseline for Gradient-Based Reinforcement Learningcited by 84
  2. 2015How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary?cited by 76
  3. 2021Creativity and Machine Learning: A Surveycited by 21
  4. 2015Quantifying Creativity in Art Networkscited by 12

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 0; 2026: 2.

6 papers.

  1. 2023Can Copyright be Reduced to Privacy?cited by 24
  2. 2023Evaluating Large Language Model Creativity from a Literary Perspectivecited by 7
  3. 2024Tackling Copyright Issues in AI Image Generation Through Originality Estimation and Genericizationcited by 6
  4. 2022Challenges in creative generative models for music: a divergence maximization perspectivecited by 3
  5. 2026Creativity in AI as Emergence from Domain-Limited Generative Modelscited by 0
  6. 2026CresOWLve: Benchmarking Creative Problem-Solving Over Real-World Knowledgecited by 0