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The natural world often follows a long-tailed data distribution where only afew classes account for most of the examples. This long-tail causes classifiersto overfit to the majority class. To mitigate this, prior solutions commonlyadopt class rebalancing strategies such as data resampling and loss reshaping.However, by treating each example within a class equally, these methods fail toaccount for the important notion of example hardness, i.e., within each classsome examples are easier to classify than others. To incorporate this notion ofhardness into the learning process, we propose the EarLy-exitingFramework(ELF). During training, ELF learns to early-exit easy examples throughauxiliary branches attached to a backbone network. This offers a dualbenefit-(1) the neural network increasingly focuses on hard examples, sincethey contribute more to the overall network loss; and (2) it frees upadditional model capacity to distinguish difficult examples. Experimentalresults on two large-scale datasets, ImageNet LT and iNaturalist'18,demonstrate that ELF can improve state-of-the-art accuracy by more than 3percent. This comes with the additional benefit of reducing up to 20 percent ofinference time FLOPS. ELF is complementary to prior work and can naturallyintegrate with a variety of existing methods to tackle the challenge oflong-tailed distributions.

ELF: An Early-Exiting Framework for Long-Tailed Classification