arXiv Machine Learning
Real-time Learning and Evolution in Robotic Art Installations
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Aug 17
From Style Replication to Style Exploration: Enabling Art Style Exploration with Analyze-Experiment-Resituate Framework
arXiv:2608. 14405v1 Announce Type: cross Abstract: Art style is a signature of professional digital artists that develops through repeated experimentation, reflection, and adaptation.
MIT News AI
Jun 29
3 Questions: Beyond data-driven aesthetics
In a new Keller Gallery exhibition, Alexandros Haridis SM ’17, PhD ’22 traces centuries of ideas about aesthetic judgment and explores how design can make complex computational systems visible.
arXiv AI
Jul 14
Model Collapse: On Recursion, Noise, and Uncharted Machine Visions
arXiv:2607. 09705v1 Announce Type: cross Abstract: Since 2023, computer scientists have warned against model collapse -- the contamination of training sets with AI-generated outputs that progressively degrade model performance.
arXiv Computer Vision
Sep 15
PaintCopilot: Modeling Painting as Autonomous Artistic Continuation
arXiv:2605.20941v2 Announce Type: replace Abstract: Existing neural painting methods are target-driven: given a reference image, strokes are optimized to reconstruct it, fixing the outcome before pai...
Hugging Face Trending Papers
Jul 22
Back to Back with a Copy: A Computational Analysis of AI-Generated Visual Contemporary Art Pastiches
The aim of this paper is twofold. First, it investigates whether newer generative models are getting better at pastiching contemporary artworks.
Hugging Face Trending Papers
Jul 9
ArtMine: Discovering and Formalizing Artistic Processes
Understanding how artworks are created requires reasoning about the iterative decisions, material operations, and contextual influences that shape artistic production. While recent generative AI systems can synthesize artworks with high fidelity, they primarily model distributions over finished artifacts rather than the creative processes underlying their creation.