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

• URL: http://arxiv.org/abs/2303.13664v1
• Date: Thu, 23 Mar 2023 20:37:25 GMT
• Title: Temperature Schedules for Self-Supervised Contrastive Methods on Long-Tail Data
• Authors: Anna Kukleva, Moritz B\"ohle, Bernt Schiele, Hilde Kuehne, Christian Rupprecht
• Abstract summary: In this paper, we analyse the behaviour of one of the most popular variants of self-supervised learning (SSL) on long-tail data. We find that a large $tau$ emphasises group-wise discrimination, whereas a small $tau$ leads to a higher degree of instance discrimination. We propose to employ a dynamic $tau$ and show that a simple cosine schedule can yield significant improvements in the learnt representations.
• Score: 87.77128754860983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 this paper, we analyse the behaviour of one of the most popular variants of SSL, i.e. contrastive methods, on long-tail data. In particular, we investigate the role of the temperature parameter $\tau$ in the contrastive loss, by analysing the loss through the lens of average distance maximisation, and find that a large $\tau$ emphasises group-wise discrimination, whereas a small $\tau$ leads to a higher degree of instance discrimination. While $\tau$ has thus far been treated exclusively as a constant hyperparameter, in this work, we propose to employ a dynamic $\tau$ 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.

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