  We propose GradTail, an algorithm that uses gradients to improve model
performance on the fly in the face of long-tailed training data distributions.
Unlike conventional long-tail classifiers which operate on converged - and
possibly overfit - models, we demonstrate that an approach based on gradient
dot product agreement can isolate long-tailed data early on during model
training and improve performance by dynamically picking higher sample weights
for that data. We show that such upweighting leads to model improvements for
both classification and regression models, the latter of which are relatively
unexplored in the long-tail literature, and that the long-tail examples found
by gradient alignment are consistent with our semantic expectations.
