  We address the problem of disentanglement of factors that generate a given
data into those that are correlated with the labeling and those that are not.
Our solution is simpler than previous solutions and employs adversarial
training. First, the part of the data that is correlated with the labels is
extracted by training a classifier. Then, the other part is extracted such that
it enables the reconstruction of the original data but does not contain label
information. The utility of the new method is demonstrated on visual datasets
as well as on financial data. Our code is available at
https://github.com/naamahadad/A-Two-Step-Disentanglement-Method
