# Towards creativity characterization of generative models via group-based subset scanning

arXiv: https://arxiv.org/abs/2104.00479 · HTML: https://mlsanity.com/paper/2104.00479/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 44 references that are in the map.
- First posted on arXiv 1 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Improving Variational Inference with Inverse Autoregressive Flow](https://mlsanity.com/paper/1606.04934/) (2016)
- [Identifying Audio Adversarial Examples via Anomalous Pattern Detection](https://mlsanity.com/paper/2002.05463/) (2020)
- [Novelty Detection with GAN](https://mlsanity.com/paper/1802.10560/) (2018)

## Built on by (most cited first, 11 of 11)

- [Weakly Supervised Detection of Hallucinations in LLM Activations](https://mlsanity.com/paper/2312.02798/) (2023)
- [TP2O: Creative Text Pair-to-Object Generation using Balance Swap-Sampling](https://mlsanity.com/paper/2310.01819/) (2023)
- [PartCraft: Crafting Creative Objects by Parts](https://mlsanity.com/paper/2407.04604/) (2024)
- [Novel Object Synthesis via Adaptive Text-Image Harmony](https://mlsanity.com/paper/2410.20823/) (2024)
- [DreamCreature: Crafting Photorealistic Virtual Creatures from Imagination](https://mlsanity.com/paper/2311.15477/) (2023)
- [Domain-agnostic and Multi-level Evaluation of Generative Models](https://mlsanity.com/paper/2301.08750/) (2023)
- [AI Art Curation: Re-imagining the city of Helsinki in occasion of its Biennial](https://mlsanity.com/paper/2306.03753/) (2023)
- [Efficient Representation of the Activation Space in Deep Neural Networks](https://mlsanity.com/paper/2312.08143/) (2023)
- [Chirpy3D: Part-Aware Multi-View Diffusion for Creative Fine-Grained Object Generation](https://mlsanity.com/paper/2501.04144/) (2025)
- [Deconstructing Generative Diversity: An Information Bottleneck Analysis of Discrete Latent Generative Models](https://mlsanity.com/paper/2512.01831/) (2025)
- [Retrieval with Multiple Query Vectors through Anomalous Pattern Detection](https://mlsanity.com/paper/2605.01965/) (2026)

## Abstract (arXiv)

Deep generative models, such as Variational Autoencoders (VAEs), have been employed widely in computational creativity research. However, such models discourage out-of-distribution generation to avoid spurious sample generation, limiting their creativity. Thus, incorporating research on human creativity into generative deep learning techniques presents an opportunity to make their outputs more compelling and human-like. As we see the emergence of generative models directed to creativity research, a need for machine learning-based surrogate metrics to characterize creative output from these models is imperative. We propose group-based subset scanning to quantify, detect, and characterize creative processes by detecting a subset of anomalous node-activations in the hidden layers of generative models. Our experiments on original, typically decoded, and "creatively decoded" (Das et al 2020) image datasets reveal that the proposed subset scores distribution is more useful for detecting creative processes in the activation space rather than the pixel space. Further, we found that creative samples generate larger subsets of anomalies than normal or non-creative samples across datasets. The node activations highlighted during the creative decoding process are different from those responsible for normal sample generation.