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.

本文提出了一种名为 Interventional Style Transfer 的方法，是针对在计算机视觉系统的实际应用过程中，包括在生物医学研究的发现过程中，需要具有一种对上下文干扰具有不变性及对新数据具有普遍适用性的因果表示的问题，在两个新的单细胞荧光显微镜数据集的内部复制结构中进行了验证，证明了该方法可以显著地提高 OOD 的泛化能力。