  In cryo-electron microscopy, accurate particle localization and
classification are imperative. Recent deep learning solutions, though
successful, require extensive training data sets. The protracted generation
time of physics-based models, often employed to produce these data sets, limits
their broad applicability. We introduce FakET, a method based on Neural Style
Transfer, capable of simulating the forward operator of any cryo transmission
electron microscope. It can be used to adapt a synthetic training data set
according to reference data producing high-quality simulated micrographs or
tilt-series. To assess the quality of our generated data, we used it to train a
state-of-the-art localization and classification architecture and compared its
performance with a counterpart trained on benchmark data. Remarkably, our
technique matches the performance, boosts data generation speed 750 times, uses
33 times less memory, and scales well to typical transmission electron
microscope detector sizes. It leverages GPU acceleration and parallel
processing. The source code is available at https://github.com/paloha/faket.
