  Most deep-learning-based image classification methods assume that all samples
are generated under an independent and identically distributed (IID) setting.
However, out-of-distribution (OOD) generalization is more common in practice,
which means an agnostic context distribution shift between training and testing
environments. To address this problem, we present a novel Partial Feature
Decorrelation Learning (PFDL) algorithm, which jointly optimizes a feature
decomposition network and the target image classification model. The feature
decomposition network decomposes feature embeddings into the independent and
the correlated parts such that the correlations between features will be
highlighted. Then, the correlated features help learn a stable feature
representation by decorrelating the highlighted correlations while optimizing
the image classification model. We verify the correlation modeling ability of
the feature decomposition network on a synthetic dataset. The experiments on
real-world datasets demonstrate that our method can improve the backbone
model's accuracy on OOD image classification datasets.
