In inverse problems, we often have access to data consisting of paired samples $(x,y)sim p_{X,Y}(x,y)$ where $y$ are partial observations of a physical system, and $x$ represents the unknowns of the problem. Under these circumstances, we can employ supervised training to learn a solution $x$ and its uncertainty from the observations $y$. We refer to this problem as the supervised case. However, the data $ysim p_{Y}(y)$ collected at one point could be distributed differently than observations $ysim p_{Y}(y)$, relevant for a current set of problems. In the context of Bayesian inference, we propose a two-step scheme, which makes use of normalizing flows and joint data to train a conditional generator $q_{theta}(x|y)$ to approximate the target posterior density $p_{X|Y}(x|y)$. Additionally, this preliminary phase provides a density function $q_{theta}(x|y)$, which can be recast as a prior for the unsupervised problem, e.g.~when only the observations $ysim p_{Y}(y)$, a likelihood model $y|x$, and a prior on $x$ are known. We then train another invertible generator with output density $q_{phi}(x|y)$ specifically for $y$, allowing us to sample from the posterior $p_{X|Y}(x|y)$. We present some synthetic results that demonstrate considerable training speedup when reusing the pretrained network $q_{theta}(x|y)$ as a warm start or preconditioning for approximating $p_{X|Y}(x|y)$, instead of learning from scratch. This training modality can be interpreted as an instance of transfer learning. This result is particularly relevant for large-scale inverse problems that employ expensive numerical simulations.
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