  In a recent paper "Deep Learning Volatility" a fast 2-step deep calibration
algorithm for rough volatility models was proposed: in the first step the time
consuming mapping from the model parameter to the implied volatilities is
learned by a neural network and in the second step standard solver techniques
are used to find the best model parameter.
  In our paper we compare these results with an alternative direct approach
where the the mapping from market implied volatilities to model parameters is
approximated by the neural network, without the need for an extra solver step.
Using a whitening procedure and a projection of the target parameter to [0,1],
in order to be able to use a sigmoid type output function we found that the
direct approach outperforms the two-step one for the data sets and methods
published in "Deep Learning Volatility".
  For our implementation we use the open source tensorflow 2 library. The paper
should be understood as a technical comparison of neural network techniques and
not as an methodically new Ansatz.
