  Deep learning is a promising, ultra-fast approach for inverse design in
nano-optics, but despite fast advancement of the field, the computational cost
of dataset generation, as well as of the training procedure itself remains a
major bottleneck. This is particularly inconvenient because new data need to be
generated and a new network needs to be trained for any modification of the
problem. We propose a technique that allows to train a single neural network on
a broad range of design targets without any re-training. The key idea of our
method is to enrich existing data with random "regions of interest" (ROI)
labels. A model trained on such ROI-decorated data becomes capable to operate
on a broad range of physical targets, while it learns to focus its design
effort on a user-defined ROI, ignoring the rest of the physical domain. We
demonstrate the method by training a tandem-network on the design of dielectric
core-shell nano-spheres for electric and magnetic dipole and quadrupole
scattering over a broad spectral range. The network learns to tailor very
distinct, flexible design targets like scattering due to specific multipoles in
narrow spectral windows. Varying the design problem does not require any
re-training. Our approach is very general and can be directly used with
existing datasets. It can be straightforwardly applied to other network
architectures and problems.
