Improving Domain Generalization in Contrastive Learning using Adaptive Temperature Control

• URL: http://arxiv.org/abs/2601.07748v1
• Date: Mon, 12 Jan 2026 17:32:24 GMT
• Title: Improving Domain Generalization in Contrastive Learning using Adaptive Temperature Control
• Authors: Robert Lewis, Katie Matton, Rosalind W. Picard, John Guttag,
• Abstract summary: Self-supervised pre-training with contrastive learning is a powerful method for learning from sparsely labeled data.<n>Performance can drop considerably when there is a shift in the distribution of data from training to test time.<n>We present a new method for contrastive learning that incorporates domain labels to increase the domain invariance of learned representations.
• Score: 6.29137812995328
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised pre-training with contrastive learning is a powerful method for learning from sparsely labeled data. However, performance can drop considerably when there is a shift in the distribution of data from training to test time. We study this phenomenon in a setting in which the training data come from multiple domains, and the test data come from a domain not seen at training that is subject to significant covariate shift. We present a new method for contrastive learning that incorporates domain labels to increase the domain invariance of learned representations, leading to improved out-of-distribution generalization. Our method adjusts the temperature parameter in the InfoNCE loss -- which controls the relative weighting of negative pairs -- using the probability that a negative sample comes from the same domain as the anchor. This upweights pairs from more similar domains, encouraging the model to discriminate samples based on domain-invariant attributes. Through experiments on a variant of the MNIST dataset, we demonstrate that our method yields better out-of-distribution performance than domain generalization baselines. Furthermore, our method maintains strong in-distribution task performance, substantially outperforming baselines on this measure.

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