  A natural way of estimating heteroscedastic label noise in regression is to
model the observed (potentially noisy) target as a sample from a normal
distribution, whose parameters can be learned by minimizing the negative
log-likelihood. This formulation has desirable loss attenuation properties, as
it reduces the contribution of high-error examples. Intuitively, this behavior
can improve robustness against label noise by reducing overfitting. We propose
an extension of this simple and probabilistic approach to classification that
has the same desirable loss attenuation properties. Furthermore, we discuss and
address some practical challenges of this extension. We evaluate the
effectiveness of the method by measuring its robustness against label noise in
classification. We perform enlightening experiments exploring the inner
workings of the method, including sensitivity to hyperparameters, ablation
studies, and other insightful analyses.
