Abstract:Projections, also known as dimensionality reduction methods, P aim to map high-dimensional data to 2D scatterplots for visual exploration. Inverse projection methods P1 aim to map this 2D space to the data space to support tasks such as data augmentation, classifier analysis, and data imputation. Current P1 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.