Temperature Schedules for Self-Supervised Contrastive Methods on Long-Tail Data

Most approaches for self-supervised learning (SSL) are optimised on curated balanced datasets, e.g. ImageNet, despite the fact that natural data usually exhibits long-tail distributions. In particular, we investigate the role of the temperature parameter τ in the contrastive loss, by analysing the loss through the lens of average distance maximisation, and find that a large τ emphasises group-wise discrimination, whereas a small τ leads to a higher degree of instance discrimination. While τ has thus far been treated exclusively as a constant hyperparameter, in this work, we propose to employ a dynamic τ and show that a simple cosine schedule can yield significant improvements in the learnt representations. Such a schedule results in a constant'task switching' between an emphasis on instance discrimination and group-wise discrimination and thereby ensures that the model learns both group-wise features, as well as instance-specific details. Since frequent classes benefit from the former, while infrequent classes require the latter, we find this method to consistently improve separation between the classes in long-tail data without any additional computational cost. Deep Neural Networks have shown remarkable capabilities at learning representations of their inputs that are useful for a variety of tasks. Especially since the advent of recent self-supervised learning (SSL) techniques, rapid progress towards learning universally useful representations has been made. Currently, however, SSL on images is mainly carried out on benchmark datasets that have been constructed and curated for supervised learning (e.g. Although the labels of curated datasets are not explicitly used in SSL, the structure of the data still follows the predefined set of classes.