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arXiv · 2111.05643

Conditional Alignment and Uniformity for Contrastive Learning with Continuous Proxy Labels

Benoit Dufumier·Pietro Gori·Julie Victor·Antoine Grigis·Edouard Duchesnay
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Abstract

Contrastive Learning has shown impressive results on natural and medical images, without requiring annotated data. However, a particularity of medical images is the availability of meta-data (such as age or sex) that can be exploited for learning representations. Here, we show that the recently proposed contrastive y-Aware InfoNCE loss, that integrates multi-dimensional meta-data, asymptotically optimizes two properties: conditional alignment and global uniformity. Similarly to [Wang, 2020], conditional alignment means that similar samples should have similar features, but conditionally on the meta-data. Instead, global uniformity means that the (normalized) features should be uniformly distributed on the unit hyper-sphere, independently of the meta-data. Here, we propose to define conditional uniformity, relying on the meta-data, that repel only samples with dissimilar meta-data. We show that direct optimization of both conditional alignment and uniformity improves the representations, in terms of linear evaluation, on both CIFAR-100 and a brain MRI dataset.

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Benoit Dufumier, Pietro Gori, Julie Victor, Antoine Grigis, Edouard Duchesnay. 2021-11-10. Conditional Alignment and Uniformity for Contrastive Learning with Continuous Proxy Labels. https://arxiv.org/abs/2111.05643

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