  Simulations of high energy density physics are expensive, largely in part for
the need to produce non-local thermodynamic equilibrium opacities.
High-fidelity spectra may reveal new physics in the simulations not seen with
low-fidelity spectra, but the cost of these simulations also scale with the
level of fidelity of the opacities being used. Neural networks are capable of
reproducing these spectra, but neural networks need data to to train them which
limits the level of fidelity of the training data. This paper demonstrates that
it is possible to reproduce high-fidelity spectra with median errors in the
realm of 3\% to 4\% using as few as 50 samples of high-fidelity Krypton data by
performing transfer learning on a neural network trained on many times more
low-fidelity data.
