mlsanityData to 7 Oct 2026

From paintbrush to pixel: A review of deep neural networks in AI-generated art

arXiv 2302.10913 ↗First posted 14 Feb 2023cs.LG cs.AIcited by 5

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

Abstract (arXiv)

This paper delves into the fascinating field of AI-generated art and explores the various deep neural network architectures and models that have been utilized to create it. From the classic convolutional networks to the cutting-edge diffusion models, we examine the key players in the field. We explain the general structures and working principles of these neural networks. Then, we showcase examples of milestones, starting with the dreamy landscapes of DeepDream and moving on to the most recent developments, including Stable Diffusion and DALL-E 3, which produce mesmerizing images. We provide a detailed comparison of these models, highlighting their strengths and limitations, and examining the remarkable progress that deep neural networks have made so far in a short period of time. With a unique blend of technical explanations and insights into the current state of AI-generated art, this paper exemplifies how art and computer science interact.

Key facts. Cited by 5 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 64 of its 116 references that are in the map. First posted on arXiv 14 Feb 2023.

Builds on

64 of 116 references.

Closest ancestors · 6

Same direction, newest first

  1. 2023Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models8 Feb
  2. 2023ChatGPT is not all you need. A State of the Art Review of large Generative AI models11 Jan
  3. 2022InstructPix2Pix: Learning to Follow Image Editing Instructions17 Nov
  4. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding23 May
  5. 2022Hierarchical Text-Conditional Image Generation with CLIP Latents13 Apr
  6. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models20 Dec

Other references · 58

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38 more
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50 most cited of 58 All 58 (JSON)

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Cited by year

Citing papers per year: 2024: 5.

5 papers.

  1. 2024Diffusion-Based Visual Art Creation: A Survey and New Perspectivescited by 17
  2. 2024HaSPeR: An Image Repository for Hand Shadow Puppet Recognitioncited by 5
  3. 2024PyPotteryLens: An Open-Source Deep Learning Framework for Automated Digitisation of Archaeological Pottery Documentationcited by 3
  4. 2024Exploring the Relationship: Transformative Adaptive Activation Functions in Comparison to Other Activation Functionscited by 1
  5. 2024GaNDLF-Synth: A Framework to Democratize Generative AI for (Bio)Medical Imagingcited by 0

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