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

arXiv: https://arxiv.org/abs/2401.10299 · HTML: https://mlsanity.com/paper/2401.10299/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 7 references that are in the map.
- First posted on arXiv 18 Jan 2024.

## Builds on (most cited first, 6 of 6)

- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [NICE: Non-linear Independent Components Estimation](https://mlsanity.com/paper/1410.8516/) (2014)
- [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)
- [An attempt to generate new bridge types from latent space of PixelCNN](https://mlsanity.com/paper/2401.05964/) (2024)

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

- [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)
- [Diffuse to Detect: A Generalizable Framework for Anomaly Detection with Diffusion Models Applications to UAVs and Beyond](https://mlsanity.com/paper/2510.22928/) (2025)

## Abstract (arXiv)

Through examples of coordinate and probability transformation between different distributions, the basic principle of normalizing flow is introduced in a simple and concise manner. From the perspective of the distribution of random variable function, the essence of probability transformation is explained, and the scaling factor Jacobian determinant of probability transformation is introduced. Treating the dataset as a sample from the population, obtaining normalizing flow is essentially through sampling surveys to statistically infer the numerical features of the population, and then the loss function is established by using the maximum likelihood estimation method. This article introduces how normalizing flow cleverly solves the two major application challenges of high-dimensional matrix determinant calculation and neural network reversible transformation. Using symmetric structured image dataset of three-span beam bridge, arch bridge, cable-stayed bridge and suspension bridge, constructing and training normalizing flow based on the Glow API in the TensorFlow Probability library. The model can smoothly transform the complex distribution of the bridge dataset into a standard normal distribution, and from the obtained latent space sampling, it can generate new bridge types that are different from the training dataset.