  Independent Component Analysis (ICA) aims to find a coordinate system in
which the components of the data are independent. In this paper we construct a
new nonlinear ICA model, called WICA, which obtains better and more stable
results than other algorithms. A crucial tool is given by a new efficient
method of verifying nonlinear dependence with the use of computation of
correlation coefficients for normally weighted data. In addition, authors
propose a new baseline nonlinear mixing to perform comparable experiments, and
a~reliable measure which allows fair comparison of nonlinear models. Our code
for WICA is available on Github https://github.com/gmum/wica.
