  We present a novel ML framework for modeling the wavelength-dependent gain of
multiple EDFAs, based on semi-supervised, self-normalizing neural networks,
enabling one-shot transfer learning. Our experiments on 22 EDFAs in Open
Ireland and COSMOS testbeds show high-accuracy transfer-learning even when
operated across different amplifier types.
