  Last-layer retraining methods have emerged as an efficient framework for
correcting existing base models. Within this framework, several methods have
been proposed to deal with correcting models for subgroup fairness with and
without group membership information. Importantly, prior work has demonstrated
that many methods are susceptible to noisy labels. To this end, we propose a
drop-in correction for label noise in last-layer retraining, and demonstrate
that it achieves state-of-the-art worst-group accuracy for a broad range of
symmetric label noise and across a wide variety of datasets exhibiting spurious
correlations. Our proposed approach uses label spreading on a latent nearest
neighbors graph and has minimal computational overhead compared to existing
methods.
