Contemporary neural networks still fall short of human-level generalization, which extends
far beyond our direct experiences. In this paper, we argue that the underlying cause for this …

Self-supervised pretraining has been shown to yield powerful representations for transfer
learning. These performance gains come at a large computational cost however, with state …

We introduce a parametric nonlinear transformation that is well-suited for Gaussianizing
data from natural images. The data are linearly transformed, and each component is then …

Many real world tasks such as reasoning and physical interaction require identification and
manipulation of conceptual entities. A first step towards solving these tasks is the automated …

A general framework for solving image inverse problems with piecewise linear estimations is
introduced in this paper. The approach is based on Gaussian mixture models, which are …

We propose an image denoising method that ex-ploits nonlocal image modeling, principal
component analysis (PCA), and local shape-adaptive anisotropic estimation. The nonlocal …

In this chapter, the basic concepts of clustering are introduced. Moreover, the most relevant
decisions to be made for the practical application of clustering methods are listed and briefly …

Spatial context in images induces perceptual phenomena associated with salience and
modulates the responses of neurons in primary visual cortex (V1). However, the …

A key hypothesis in sensory system neuroscience is that sensory representations are
adapted to the statistical regularities in sensory signals and thereby incorporate knowledge …

Accurate identification of the working conditions of froth flotation remains challenging
because of the inherent chaotic nature of the underlying microscopic phenomenon. The froth …