  Neural networks employ spurious correlations in their predictions, resulting
in decreased performance when these correlations do not hold. Recent works
suggest fixing pretrained representations and training a classification head
that does not use spurious features. We investigate how spurious features are
represented in pretrained representations and explore strategies for removing
information about spurious features. Considering the Waterbirds dataset and a
few pretrained representations, we find that even with full knowledge of
spurious features, their removal is not straightforward due to entangled
representation. To address this, we propose a linear autoencoder training
method to separate the representation into core, spurious, and other features.
We propose two effective spurious feature removal approaches that are applied
to the encoding and significantly improve classification performance measured
by worst group accuracy.
