  The Long-Tailed Recognition (LTR) problem emerges in the context of learning
from highly imbalanced datasets, in which the number of samples among different
classes is heavily skewed. LTR methods aim to accurately learn a dataset
comprising both a larger Head set and a smaller Tail set. We propose a theorem
where under the assumption of strong convexity of the loss function, the
weights of a learner trained on the full dataset are within an upper bound of
the weights of the same learner trained strictly on the Head. Next, we assert
that by treating the learning of the Head and Tail as two separate and
sequential steps, Continual Learning (CL) methods can effectively update the
weights of the learner to learn the Tail without forgetting the Head. First, we
validate our theoretical findings with various experiments on the toy MNIST-LT
dataset. We then evaluate the efficacy of several CL strategies on multiple
imbalanced variations of two standard LTR benchmarks (CIFAR100-LT and
CIFAR10-LT), and show that standard CL methods achieve strong performance gains
in comparison to baselines and approach solutions that have been tailor-made
for LTR. We also assess the applicability of CL techniques on real-world data
by exploring CL on the naturally imbalanced Caltech256 dataset and demonstrate
its superiority over state-of-the-art classifiers. Our work not only unifies
LTR and CL but also paves the way for leveraging advances in CL methods to
tackle the LTR challenge more effectively.
