Summary of Machine Apophenia: the Kaleidoscopic Generation Of Architectural Images, by Alexey Tikhonov and Dmitry Sinyavin
Machine Apophenia: The Kaleidoscopic Generation of Architectural Images
by Alexey Tikhonov, Dmitry Sinyavin
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed methodology combines multiple neural networks to generate unique architectural images without human supervision or moderation. The approach is rooted in machine apophenia, a conceptual framework that suggests neural networks trained on diverse data internalize aesthetic preferences, producing coherent designs even from random inputs. The iterative process involves image generation, description, and refinement, resulting in captioned postcards shared on social media platforms. Evaluation studies demonstrate improvements in both technical and aesthetic metrics at each step. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses artificial intelligence to create unique architectural images without human control. It’s like a computer program that learns what makes good designs and creates new ones based on what it sees. The researchers used a special way of training the computers called machine apophenia, which helps them make sense of design styles. They tested their idea by having the computers generate lots of different images and then asked people to rate how well they liked each one. The results showed that the computer-generated designs got better and better as it went along. |
Keywords
» Artificial intelligence » Image generation
