An attempt to generate new bridge types from latent space of PixelCNN
Hongjun Zhang
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.