  Recent work demonstrated that flow-based invertible neural networks are
promising tools for solving ambiguous inverse problems. Following up on this,
we investigate how ten invertible architectures and related models fare on two
intuitive, low-dimensional benchmark problems, obtaining the best results with
coupling layers and simple autoencoders. We hope that our initial efforts
inspire other researchers to evaluate their invertible architectures in the
same setting and put forth additional benchmarks, so our evaluation may
eventually grow into an official community challenge.
