Jeremiah and Laura Johnson

Statement

These works are collaborations between the husband and wife team of Jeremiah Johnson, a computational mathematician, and Laura Johnson, a painter. Jeremiah developed a generative neural network that uses a sequence of linear and nonlinear transformations to produce images from randomly generated input vectors. Such visualizations can be helpful in understanding the transformation a neural network produces. In these works, the parameters of the linear transformations are sampled from Gaussians. Hyperbolic tangents provide the nonlinear transformations. The raw images this network produces are often interesting, but with careful manual cropping to balance the compositions and to focus attention on compelling aspects, they become art.

Artworks

This is a collaborative piece produced in two stages. First, a generative neural network was used to generate a raw image. The neural network is a function $\mathcal{F}:\mathbb{R}^p \to \mathbb{R}^{m\times n}$ that maps a latent vector through a sequence of linear and nonlinear transformations to produce the raw image. The parameters of the linear transformations are sampled from Gaussians, hyperbolic tangents provide the nonlinear transformations, and the network is not optimized in any way; as such, the raw output image provides a way to visualize the transformation the network implements before it has been trained. We then manually cropped the raw image to balance the composition and to focus on a particularly compelling aspect of it.