In artificial intelligence ‘Latent Space’ refers to a mathematical space which maps what
a neural network has learnt from training images. Once it has been trained it
understands all images of trees as existing in a specific area, and all images of birds
The neural network can be reverse engineered to create fake images from these
coordinates1. But what if it was given a new path to travel
between these recognisable points, instead moving through the in-between space?
Latent Space is a video snap-shot of an A.I. algorithm in its infancy trained using 14.2
million photographs2 continuously producing new images.
1) Plug & Play Generative Networks: Conditional Iterative Generation of Images
in Latent Space (2016)
2) ImageNet: A Large-Scale Hierarchical Image Database (2009)
Special thanks to Anh Nguyen et al. at Evolving-AI for their