Out of Distribution Generalization via Interventional Style Transfer in
  Single-Cell Microscopy
Wolfgang M. Pernice Michael Doron Alex Quach Aditya Pratapa Sultan Kenjeyev Nicholas De Veaux Michio Hirano Juan C. Caicedo
Real-world deployment of computer vision systems, including in the discovery
processes of biomedical research, requires causal representations that are
invariant to contextual nuisances and generalize to new data. Leveraging the
internal replicate structure of two novel single-cell fluorescent microscopy
datasets, we propose generally applicable tests to assess the extent to which
models learn causal representations across increasingly challenging levels of
OOD-generalization. We show that despite seemingly strong performance, as
assessed by other established metrics, both naive and contemporary baselines
designed to ward against confounding, collapse on these tests. We introduce a
new method, Interventional Style Transfer (IST), that substantially improves
OOD generalization by generating interventional training distributions in which
spurious correlations between biological causes and nuisances are mitigated. We
publish our code and datasets.