mlsanityData to 7 Oct 2026

CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms

arXiv 1706.07068 ↗First posted 21 Jun 2017cs.AIcited by 96

Not on the map. Why?

Abstract (arXiv)

We propose a new system for generating art. The system generates art by looking at art and learning about style; and becomes creative by increasing the arousal potential of the generated art by deviating from the learned styles. We build over Generative Adversarial Networks (GAN), which have shown the ability to learn to generate novel images simulating a given distribution. We argue that such networks are limited in their ability to generate creative products in their original design. We propose modifications to its objective to make it capable of generating creative art by maximizing deviation from established styles and minimizing deviation from art distribution. We conducted experiments to compare the response of human subjects to the generated art with their response to art created by artists. The results show that human subjects could not distinguish art generated by the proposed system from art generated by contemporary artists and shown in top art fairs. Human subjects even rated the generated images higher on various scales.

Key facts. Cited by 96 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jun 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 23; 2022: 9; 2023: 15; 2024: 25; 2025: 18; 2026: 6.

96 papers.

Sort:

By area:

  1. 2023Self-Consuming Generative Models Go MADcited by 239
  2. 2021Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesiscited by 140
  3. 2021Deep Generative Models in Engineering Design: A Reviewcited by 103
  4. 2021Understanding and Creating Art with AI: Review and Outlookcited by 60
  5. 2021Copyright in Generative Deep Learningcited by 29
  6. 2023A Mathematical Abstraction for Balancing the Trade-off Between Creativity and Reality in Large Language Modelscited by 29
  7. 2023ConceptLab: Creative Concept Generation using VLM-Guided Diffusion Prior Constraintscited by 25
  8. 2021CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesiscited by 24
  9. 2021Creativity and Machine Learning: A Surveycited by 21
  10. 2023Deep Learning in Deterministic Computational Mechanicscited by 21
  11. 2024Evaluating Creative Short Story Generation in Humans and Large Language Modelscited by 21
  12. 2024PMG : Personalized Multimodal Generation with Large Language Modelscited by 19
38 more
  1. 2024Can AI Be as Creative as Humans?cited by 17
  2. 2024Evaluating Diversity in Automatic Poetry Generationcited by 17
  3. 2024Diffusion-Based Visual Art Creation: A Survey and New Perspectivescited by 17
  4. 2023TP2O: Creative Text Pair-to-Object Generation using Balance Swap-Samplingcited by 16
  5. 2021GAN Computers Generate Arts? A Survey on Visual Arts, Music, and Literary Text Generation using Generative Adversarial Networkcited by 15
  6. 2024LLM-based multi-agent poetry generation in non-cooperative environmentscited by 15
  7. 2025Multimodal LLMs Can Reason about Aesthetics in Zero-Shotcited by 14
  8. 2021Learning quantum data with the quantum Earth Mover's distancecited by 13
  9. 2024PartCraft: Crafting Creative Objects by Partscited by 13
  10. 2025Enhancing Creative Generation on Stable Diffusion-based Modelscited by 13
  11. 2021Active Divergence with Generative Deep Learning -- A Survey and Taxonomycited by 12
  12. 2024Novel Object Synthesis via Adaptive Text-Image Harmonycited by 12
  13. 2023Learning to Evaluate the Artness of AI-generated Imagescited by 10
  14. 2022Toward Modeling Creative Processes for Algorithmic Paintingcited by 9
  15. 2021Selective Pseudo-label Clusteringcited by 8
  16. 2023Diverse Multimedia Layout Generation with Multi Choice Learningcited by 8
  17. 2024Creativity in AI: Progresses and Challengescited by 7
  18. 2021CIZSL++: Creativity Inspired Generative Zero-Shot Learningcited by 6
  19. 2021Generative Art Using Neural Visual Grammars and Dual Encoderscited by 6
  20. 2022DeepCreativity: Measuring Creativity with Deep Learning Techniquescited by 6
  21. 2023Simple U-net Based Synthetic Polyp Image Generation: Polyp to Negative and Negative to Polypcited by 6
  22. 2023From paintbrush to pixel: A review of deep neural networks in AI-generated artcited by 5
  23. 2023DreamCreature: Crafting Photorealistic Virtual Creatures from Imaginationcited by 5
  24. 2024What's Next? Exploring Utilization, Challenges, and Future Directions of AI-Generated Image Tools in Graphic Designcited by 5
  25. 2025Generative AI in Depth: A Survey of Recent Advances, Model Variants, and Real-World Applicationscited by 5
  26. 2021A deep learning approach to clustering visual artscited by 4
  27. 2021Detecting Visual Design Principles in Art and Architecture through Deep Convolutional Neural Networkscited by 4
  28. 2021Human in the Loop for Machine Creativitycited by 4
  29. 2022ViNTER: Image Narrative Generation with Emotion-Arc-Aware Transformercited by 4
  30. 2024RU-AI: A Large Multimodal Dataset for Machine-Generated Content Detectioncited by 4
  31. 2021A Brief Introduction to Generative Modelscited by 3
  32. 2024Negative Token Merging: Image-based Adversarial Feature Guidancecited by 3
  33. 2025WP-CLIP: Leveraging CLIP to Predict W"olfflin's Principles in Visual Artcited by 3
  34. 2025VLM-Guided Adaptive Negative Prompting for Creative Generationcited by 3
  35. 2021Projection: A Mechanism for Human-like Reasoning in Artificial Intelligencecited by 2
  36. 2021Imaginative Walks: Generative Random Walk Deviation Loss for Improved Unseen Learning Representationcited by 2
  37. 2021Object Retrieval and Localization in Large Art Collections using Deep Multi-Style Feature Fusion and Iterative Votingcited by 2
  38. 2023Controllable Multi-domain Semantic Artwork Synthesiscited by 2

50 most cited of 96 All 96 (JSON)