LCIP: Loss-controlled inverse projection of high-dimensional image data

Projections, also known as dimensionality reduction methods, P aim to map high-dimensional data to 2D scatterplots for visual exploration. Inverse projection methods P − 1 aim to map this 2D space to the data space to support tasks such as data augmentation, classifier analysis, and data imputation. Current P − 1 methods suffer from a fundamental limitation – they can only generate a fixed surface-like structure in data space, which poorly covers the richness of this space. We address this by a new method that ‘sweeps’ the data space by a surface that is not fixed but under user control. Our method works generically for any technique P and dataset, is controlled by two intuitive user-set parameters, and is simple to implement. We demonstrate it by an extensive application involving image manipulation for style transfer.