  There is an inescapable long-tailed class-imbalance issue in many real-world
classification problems. Current methods for addressing this problem only
consider scenarios where all examples come from the same distribution. However,
in many cases, there are multiple domains with distinct class imbalance. We
study this multi-domain long-tailed learning problem and aim to produce a model
that generalizes well across all classes and domains. Towards that goal, we
introduce TALLY, a method that addresses this multi-domain long-tailed learning
problem. Built upon a proposed selective balanced sampling strategy, TALLY
achieves this by mixing the semantic representation of one example with the
domain-associated nuisances of another, producing a new representation for use
as data augmentation. To improve the disentanglement of semantic
representations, TALLY further utilizes a domain-invariant class prototype that
averages out domain-specific effects. We evaluate TALLY on several benchmarks
and real-world datasets and find that it consistently outperforms other
state-of-the-art methods in both subpopulation and domain shift. Our code and
data have been released at https://github.com/huaxiuyao/TALLY.
