Before revision:  We present specific architectures for price approximation and calibration and optimize these with respect to different objectives regarding accuracy, speed and robustness . We also find that including the intermediate step of learning pricing functions of (classical or rough) models before calibration significantly improves network performance compared to direct calibration to data .
After revision:  We present specific architectures for price approximation and calibration and optimize these with respect to different objectives regarding accuracy, speed , robustness and generality and also its potentials towards model recognition .