  We present a neural network based calibration method that performs the
calibration task within a few milliseconds for the full implied volatility
surface. The framework is consistently applicable throughout a range of
volatility models -including the rough volatility family- and a range of
derivative contracts. The aim of neural networks in this work is an off-line
approximation of complex pricing functions, which are difficult to represent or
time-consuming to evaluate by other means. We highlight how this perspective
opens new horizons for quantitative modelling: The calibration bottleneck posed
by a slow pricing of derivative contracts is lifted. This brings several
numerical pricers and model families (such as rough volatility models) within
the scope of applicability in industry practice. The form in which information
from available data is extracted and stored influences network performance:
This approach is inspired by representing the implied volatility and option
prices as a collection of pixels. In a number of applications we demonstrate
the prowess of this modelling approach regarding accuracy, speed, robustness
and generality and also its potentials towards model recognition.
