ELF: An Early-Exiting Framework for Long-Tailed Classification
It addresses the challenge of overfitting to majority classes in long-tailed data distributions for machine learning applications, offering an incremental improvement by integrating with existing methods.
The paper tackles the problem of long-tailed classification by proposing an early-exiting framework that focuses on hard examples, improving state-of-the-art accuracy by over 3% on datasets like ImageNet LT and iNaturalist'18 while reducing inference time FLOPS by up to 20%.
The natural world often follows a long-tailed data distribution where only a few classes account for most of the examples. This long-tail causes classifiers to overfit to the majority class. To mitigate this, prior solutions commonly adopt class rebalancing strategies such as data resampling and loss reshaping. However, by treating each example within a class equally, these methods fail to account for the important notion of example hardness, i.e., within each class some examples are easier to classify than others. To incorporate this notion of hardness into the learning process, we propose the EarLy-exiting Framework(ELF). During training, ELF learns to early-exit easy examples through auxiliary branches attached to a backbone network. This offers a dual benefit-(1) the neural network increasingly focuses on hard examples, since they contribute more to the overall network loss; and (2) it frees up additional model capacity to distinguish difficult examples. Experimental results on two large-scale datasets, ImageNet LT and iNaturalist'18, demonstrate that ELF can improve state-of-the-art accuracy by more than 3 percent. This comes with the additional benefit of reducing up to 20 percent of inference time FLOPS. ELF is complementary to prior work and can naturally integrate with a variety of existing methods to tackle the challenge of long-tailed distributions.