  A major challenge in machine learning is resilience to out-of-distribution
data, that is data that exists outside of the distribution of a model's
training data. Training is often performed using limited, carefully curated
datasets and so when a model is deployed there is often a significant
distribution shift as edge cases and anomalies not included in the training
data are encountered. To address this, we propose the Input Optimisation
Network, an image preprocessing model that learns to optimise input data for a
specific target vision model. In this work we investigate several
out-of-distribution scenarios in the context of semantic segmentation for
autonomous vehicles, comparing an Input Optimisation based solution to existing
approaches of finetuning the target model with augmented training data and an
adversarially trained preprocessing model. We demonstrate that our approach can
enable performance on such data comparable to that of a finetuned model, and
subsequently that a combined approach, whereby an input optimization network is
optimised to target a finetuned model, delivers superior performance to either
method in isolation. Finally, we propose a joint optimisation approach, in
which input optimization network and target model are trained simultaneously,
which we demonstrate achieves significant further performance gains,
particularly in challenging edge-case scenarios. We also demonstrate that our
architecture can be reduced to a relatively compact size without a significant
performance impact, potentially facilitating real time embedded applications.
