  Photometric data-driven classification of supernovae becomes a challenge due
to the appearance of real-time processing of big data in astronomy. Recent
studies have demonstrated the superior quality of solutions based on various
machine learning models. These models learn to classify supernova types using
their light curves as inputs. Preprocessing these curves is a crucial step that
significantly affects the final quality. In this talk, we study the application
of multilayer perceptron (MLP), bayesian neural network (BNN), and normalizing
flows (NF) to approximate observations for a single light curve. We use these
approximations as inputs for supernovae classification models and demonstrate
that the proposed methods outperform the state-of-the-art based on Gaussian
processes applying to the Zwicky Transient Facility Bright Transient Survey
light curves. MLP demonstrates similar quality as Gaussian processes and speed
increase. Normalizing Flows exceeds Gaussian processes in terms of
approximation quality as well.
