Probabilistic Contrastive Learning with Explicit Concentration on the Hypersphere
representation learning;von Mises–Fisher distribution;uncertainty;probabilistic contrastive learning
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We proposed an unnormalized and regularized form of von Mises–Fisher distribution for probabilistic contrastive learning
@misc{
li2024probabilistic,
title={Probabilistic Contrastive Learning with Explicit Concentration on the Hypersphere},
author={Hongwei Bran Li and Cheng Ouyang and Tamaz Amiranashvili and Matthew Rosen and bjoern menze and Juan Eugenio Iglesias},
year={2024},
url={https://openreview.net/forum?id=SXXOMpPh0u}
}
Contrastive learning is predominantly deterministic, limiting its effectiveness in noisy and uncertain environments. We propose a probabilistic approach inspired by the von Mises-Fisher (vMF) distribution, embedding representations on a hyperspherical space. To address numerical instability, we introduce an unnormalized and regularized vMF distribution, preserving essential properties with theoretical guarantees. 
The concentration parameter, $\kappa$, serves as an interpretable measure of aleatoric uncertainty.
Empirical evaluations show a strong correlation between estimated $\kappa$ and unseen data corruption severity, enabling effective failure analysis and enhancing out-of-distribution detection without modeling epistemic uncertainty. From a fresh perspective, our approach introduces a flexible alignment mechanism for improved uncertainty estimation in high-dimensional spaces while remaining compatible with existing contrastive learning frameworks.
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~Hongwei_Bran_Li1 ~Cheng_Ouyang2 ~Tamaz_Amiranashvili1 ~Matthew_Rosen2 ~bjoern_menze1 ~Juan_Eugenio_Iglesias1
Hongwei Bran Li Cheng Ouyang Tamaz Amiranashvili Matthew Rosen bjoern menze Juan Eugenio Iglesias
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representation learning von Mises–Fisher distribution uncertainty probabilistic contrastive learning
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li|probabilistic_contrastive_learning_with_explicit_concentration_on_the_hypersphere
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unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Probabilistic Contrastive Learning with Explicit Concentration on the Hypersphere
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