  The method presented extends a given regression neural network to make its
performance improve. The modification affects the learning procedure only,
hence the extension may be easily omitted during evaluation without any change
in prediction. It means that the modified model may be evaluated as quickly as
the original one but tends to perform better.
  This improvement is possible because the modification gives better expressive
power, provides better behaved gradients and works as a regularization. The
knowledge gained by the temporarily extended neural network is contained in the
parameters shared with the original neural network.
  The only cost is an increase in learning time.
