# An attempt to generate new bridge types from latent space of PixelCNN

arXiv: https://arxiv.org/abs/2401.05964 · HTML: https://mlsanity.com/paper/2401.05964/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 8 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [Conditional Image Generation with PixelCNN Decoders](https://mlsanity.com/paper/1606.05328/) (2016)
- [Pixel Recurrent Neural Networks](https://mlsanity.com/paper/1601.06759/) (2016)
- [PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications](https://mlsanity.com/paper/1701.05517/) (2017)
- [An attempt to generate new bridge types from latent space of variational autoencoder](https://mlsanity.com/paper/2311.03380/) (2023)
- [An attempt to generate new bridge types from latent space of generative adversarial network](https://mlsanity.com/paper/2401.00700/) (2024)

## Built on by (most cited first, 4 of 4)

- [An attempt to generate new bridge types from latent space of generative flow](https://mlsanity.com/paper/2401.10299/) (2024)
- [An attempt to generate new bridge types from latent space of denoising diffusion Implicit model](https://mlsanity.com/paper/2402.07129/) (2024)
- [An attempt to generate new bridge types from latent space of energy-based model](https://mlsanity.com/paper/2401.17657/) (2024)
- [Aided design of bridge aesthetics based on Stable Diffusion fine-tuning](https://mlsanity.com/paper/2409.15812/) (2024)

## Abstract (arXiv)

Try to generate new bridge types using generative artificial intelligence technology. Using symmetric structured image dataset of three-span beam bridge, arch bridge, cable-stayed bridge and suspension bridge , based on Python programming language, TensorFlow and Keras deep learning platform framework , PixelCNN is constructed and trained. The model can capture the statistical structure of the images and calculate the probability distribution of the next pixel when the previous pixels are given. From the obtained latent space sampling, new bridge types different from the training dataset can be generated. PixelCNN can organically combine different structural components on the basis of human original bridge types, creating new bridge types that have a certain degree of human original ability. Autoregressive models cannot understand the meaning of the sequence, while multimodal models combine regression and autoregressive models to understand the sequence. Multimodal models should be the way to achieve artificial general intelligence in the future.