# Measuring Diversity in Co-creative Image Generation

arXiv: https://arxiv.org/abs/2403.13826 · HTML: https://mlsanity.com/paper/2403.13826/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 6 Mar 2024.

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

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Reliable Fidelity and Diversity Metrics for Generative Models](https://mlsanity.com/paper/2002.09797/) (2020)
- [The Creativity of Text-to-Image Generation](https://mlsanity.com/paper/2206.02904/) (2022)
- [A Collaborative, Interactive and Context-Aware Drawing Agent for Co-Creative Design](https://mlsanity.com/paper/2209.12588/) (2022)
- [Seeding Diversity into AI Art](https://mlsanity.com/paper/2205.00804/) (2022)
- [Affect-Conditioned Image Generation](https://mlsanity.com/paper/2302.09742/) (2023)

## Built on by (most cited first, 15 of 15)

- [Test-time Alignment of Diffusion Models without Reward Over-optimization](https://mlsanity.com/paper/2501.05803/) (2025)
- [Do Less, Achieve More: Do We Need Every-Step Optimization for RL Fine-tuning of Diffusion Models?](https://mlsanity.com/paper/2605.15855/) (2026)
- [Diverse Video Generation with Determinantal Point Process-Guided Policy Optimization](https://mlsanity.com/paper/2511.20647/) (2025)
- [LoRAverse: A Submodular Framework to Retrieve Diverse Adapters for Diffusion Models](https://mlsanity.com/paper/2510.15022/) (2025)
- [Dataset Diversity Metrics and Impact on Classification Models](https://mlsanity.com/paper/2603.15276/) (2026)
- [Explore or Converge? Stage-Guided Per-Step Optimization for Diffusion Models](https://mlsanity.com/paper/2608.06768/) (2026)
- [Grassmannian Geometry Meets Dynamic Mode Decomposition in DMD-GEN: A New Metric for Mode Collapse in Time Series Generative Models](https://mlsanity.com/paper/2412.11292/) (2024)
- [Dialogue with the Machine and Dialogue with the Art World: Evaluating Generative AI for Culturally-Situated Creativity](https://mlsanity.com/paper/2412.14077/) (2024)
- [Diffusion-Classifier Synergy: Reward-Aligned Learning via Mutual Boosting Loop for FSCIL](https://mlsanity.com/paper/2510.03608/) (2025)
- [Measuring What Matters Beyond Text: Evaluating Multimodal Summaries by Quality, Alignment, and Diversity](https://mlsanity.com/paper/2605.11693/) (2026)
- [PAST: Prompt-Adaptive Sampling Termination for Efficient Diffusion Model](https://mlsanity.com/paper/2608.06794/) (2026)
- [RankT2I: A Submodular Framework for Discovering Interpretable and Diverse Semantics in Text-to-Image Models](https://mlsanity.com/paper/2608.14226/) (2026)
- [SwiftExplorer: Training-free Diffusion Model Alignment with Swift Diversity Exploration](https://mlsanity.com/paper/2609.06651/) (2026)
- [Uncertainty DMD: Restoring Diversity in Few-Step Autoregressive Video Distillation](https://mlsanity.com/paper/2609.11265/) (2026)
- [DiVid: Diagnosing Dimension-Specific Diversity Collapse in Video Generation Models](https://mlsanity.com/paper/2610.01661/) (2026)

## Abstract (arXiv)

Quality and diversity have been proposed as reasonable heuristics for assessing content generated by co-creative systems, but to date there has been little agreement around what constitutes the latter or how to measure it. Proposed approaches for assessing generative models in terms of diversity have limitations in that they compare the model's outputs to a ground truth that in the era of large pre-trained generative models might not be available, or entail an impractical number of computations. We propose an alternative based on entropy of neural network encodings for comparing diversity between sets of images that does not require ground-truth knowledge and is easy to compute. We also compare two pre-trained networks and show how the choice relates to the notion of diversity that we want to evaluate. We conclude with a discussion of the potential applications of these measures for ideation in interactive systems, model evaluation, and more broadly within computational creativity.