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arXiv · 2602.14272

Radial-VCReg: More Informative Representation Learning Through Radial Gaussianization

Yilun Kuang·Yash Dagade·Deep Chakraborty·Erik Learned-Miller·Randall Balestriero·Tim G. J. Rudner·Yann LeCun
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Abstract

Self-supervised learning aims to learn maximally informative representations, but explicit information maximization is hindered by the curse of dimensionality. Existing methods like VCReg address this by regularizing first and second-order feature statistics, which cannot fully achieve maximum entropy. We propose Radial-VCReg, which augments VCReg with a radial Gaussianization loss that aligns feature norms with the Chi distribution-a defining property of high-dimensional Gaussians. We prove that Radial-VCReg transforms a broader class of distributions towards normality compared to VCReg and show on synthetic and real-world datasets that it consistently improves performance by reducing higher-order dependencies and promoting more diverse and informative representations.

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Yilun Kuang, Yash Dagade, Deep Chakraborty, Erik Learned-Miller, Randall Balestriero, Tim G. J. Rudner, Yann LeCun. 2026-02-15. Radial-VCReg: More Informative Representation Learning Through Radial Gaussianization. https://arxiv.org/abs/2602.14272

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