Invariant Representation via Decoupling Style and Spurious Features from Images
arxiv(2023)
摘要
This paper considers the out-of-distribution (OOD) generalization problem
under the setting that both style distribution shift and spurious features
exist and domain labels are missing. This setting frequently arises in
real-world applications and is underlooked because previous approaches mainly
handle either of these two factors. The critical challenge is decoupling style
and spurious features in the absence of domain labels. To address this
challenge, we first propose a structural causal model (SCM) for the image
generation process, which captures both style distribution shift and spurious
features. The proposed SCM enables us to design a new framework called IRSS,
which can gradually separate style distribution and spurious features from
images by introducing adversarial neural networks and multi-environment
optimization, thus achieving OOD generalization. Moreover, it does not require
additional supervision (e.g., domain labels) other than the images and their
corresponding labels. Experiments on benchmark datasets demonstrate that IRSS
outperforms traditional OOD methods and solves the problem of Invariant risk
minimization (IRM) degradation, enabling the extraction of invariant features
under distribution shift.
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