mlsanityData to 7 Oct 2026

Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines?

arXiv 2301.11722 ↗First posted 27 Jan 2023cs.AI cs.HCcited by 10

On the map: Diffusion models → Image generation, text-to-image and editing

Abstract (arXiv)

An important milestone for AI is the development of algorithms that can produce drawings that are indistinguishable from those of humans. Here, we adapt the 'diversity vs. recognizability' scoring framework from Boutin et al, 2022 and find that one-shot diffusion models have indeed started to close the gap between humans and machines. However, using a finer-grained measure of the originality of individual samples, we show that strengthening the guidance of diffusion models helps improve the humanness of their drawings, but they still fall short of approximating the originality and recognizability of human drawings. Comparing human category diagnostic features, collected through an online psychophysics experiment, against those derived from diffusion models reveals that humans rely on fewer and more localized features. Overall, our study suggests that diffusion models have significantly helped improve the quality of machine-generated drawings; however, a gap between humans and machines remains -- in part explainable by discrepancies in visual strategies.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 40 of its 73 references that are in the map. First posted on arXiv 27 Jan 2023.

Builds on

40 of 73 references.

Closest ancestors · 2

Same direction, newest first

  1. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding23 May
  2. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models20 Dec

Other references · 38

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  5. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  6. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  7. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  8. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  9. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  10. 2022Scalable Diffusion Models with Transformerscited by 5,931
  11. 2022Classifier-Free Diffusion Guidancecited by 5,717
  12. 2022Generative Adversarial Networkscited by 5,603
26 more
  1. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  2. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  3. 2013Auto-Encoding Variational Bayescited by 3,980
  4. 2022A ConvNet for the 2020scited by 3,545
  5. 2017Axiomatic Attribution for Deep Networkscited by 3,350
  6. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  7. 2017FiLM: Visual Reasoning with a General Conditioning Layercited by 2,286
  8. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
  9. 2015Spatial Transformer Networkscited by 1,423
  10. 2017SmoothGrad: removing noise by adding noisecited by 1,097
  11. 2020NVAE: A Deep Hierarchical Variational Autoencodercited by 635
  12. 2017Data Augmentation Generative Adversarial Networkscited by 278
  13. 2021Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Modelscited by 251
  14. 2021Vision Transformer for Small-Size Datasetscited by 122
  15. 2016Towards a Neural Statisticiancited by 119
  16. 2022Human alignment of neural network representationscited by 74
  17. 2021Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysiscited by 65
  18. 2022Harmonizing the object recognition strategies of deep neural networks with humanscited by 59
  19. 2019The Omniglot challenge: a 3-year progress reportcited by 54
  20. 2016One-Shot Generalization in Deep Generative Modelscited by 44
  21. 2022Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysiscited by 38
  22. 2022Few-Shot Diffusion Modelscited by 37
  23. 2022Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measurecited by 33
  24. 2020Linear Attention Mechanism: An Efficient Attention for Semantic Segmentationcited by 21
  25. 2022Diversity vs. Recognizability: Human-like generalization in one-shot generative modelscited by 9
  26. 2022Exploring Transformer Backbones for Image Diffusion Modelscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 1; 2025: 4; 2026: 1.

10 papers.

  1. 2023Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directionscited by 123
  2. 2023Scaling Laws of Synthetic Images for Model Training ... for Nowcited by 107
  3. 2023Unlocking Feature Visualization for Deeper Networks with MAgnitude Constrained Optimizationcited by 26
  4. 2023TP2O: Creative Text Pair-to-Object Generation using Balance Swap-Samplingcited by 16
  5. 2024Latent Representation Matters: Human-like Sketches in One-shot Drawing Taskscited by 3
  6. 2025Sealing The Backdoor: Unlearning Adversarial Text Triggers In Diffusion Models Using Knowledge Distillationcited by 2
  7. 2025From Sound to Sight: Towards AI-authored Music Videoscited by 2
  8. 2025Back to the Baseline: Examining Baseline Effects on Explainability Metricscited by 1
  9. 2025Stable diffusion models reveal a persisting human and AI gap in visual creativitycited by 0
  10. 2026Not Too Generative, Not Too Discriminative: The Human Alignment Sweet Spotcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022A Survey on Generative Diffusion Model
  2. 2023Measuring the Success of Diffusion Models at Imitating Human Artists
  3. 2025Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images
  4. 2023State of the Art on Diffusion Models for Visual Computing
  5. 2024Organic or Diffused: Can We Distinguish Human Art from AI-generated Images?
  6. 2024Replication in Visual Diffusion Models: A Survey and Outlook
  7. 2024An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization
  8. 2024Generative AI in Vision: A Survey on Models, Metrics and Applications