Title: A margin-based replacement for cross-entropy loss
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: deep learning, loss functions, robustness, generalisation, image classification, imbalanced data, continual learning, semantic segmentation

Abstract: Cross-entropy (CE) loss is the de-facto standard for training deep neural networks (DNNs) to perform classification. Here, we propose an alternative loss, high error margin (HEM), that is more effective than CE across a range of tasks: unknown class rejection, adversarial robustness, learning with imbalanced data, continual learning, and semantic segmentation (a pixel-wise classification task). HEM loss is evaluated extensively using a wide range of DNN architectures and benchmark datasets. Despite all the experimental settings, such as the training hyper-parameters, being chosen for CE loss, HEM is inferior to CE only in terms of clean and corrupt image classification with balanced training data, and this difference is small. We also compare HEM to specialised losses that have previously been proposed to improve performance for specific tasks. LogitNorm, a loss achieving state-of-the-art performance on unknown class rejection, produces similar performance to HEM for this task, but is much poorer for continual learning and semantic segmentation. Logit-adjusted loss, designed for imbalanced data, has superior results to HEM for that task, but performs worse on unknown class rejection and semantic segmentation. DICE, a popular loss for semantic segmentation, is inferior to HEM loss on all tasks, including semantic segmentation. Thus, HEM often out-performs specialised losses, and in contrast to them, is a general-purpose replacement for CE loss.

TL;DR: Proposes a new loss function that produces better performance than CE loss on a wide range of image classification tasks