In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

We introduce a parametric nonlinear transformation that is well-suited for Gaussianizing data from natural images. After a linear transformation of the data, each component is normalized by a pooled activity measure, computed by exponentiating a weighted sum of rectified and exponentiated components and an additive constant. We optimize the parameters of this transformation (linear transform, exponents, weights, constant) over a database of natural images, directly minimizing the negentropy of the responses. We find that the optimized transformation successfully Gaussianizes the data, achieving a significantly smaller mutual information between transformed components than previous methods including ICA and radial Gaussianization. The transformation is differentiable and can be efficiently inverted, and thus induces a density model on images. We show that samples of this model are visually similar to samples of natural image patches. We also demonstrate the use of the model as a prior density in removing additive noise. Finally, we show that the transformation can be cascaded, with each layer optimized (unsupervised) using the same Gaussianization objective, to capture additional probabilistic structure.
GENERALIZED NORMALIZATION TRANSFORMATION