Joint Embedding Variational Bayes

• URL: http://arxiv.org/abs/2602.05639v1
• Date: Thu, 05 Feb 2026 13:18:53 GMT
• Title: Joint Embedding Variational Bayes
• Authors: Amin Oji, Paul Fieguth,
• Abstract summary: Variational Joint Embedding (VJE) is a framework that synthesizes joint embedding and variational inference.<n>VJE enables self-supervised learning of probabilistic representations in a reconstruction-free, non-contrastive setting.
• Score: 0.08594140167290097
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We introduce Variational Joint Embedding (VJE), a framework that synthesizes joint embedding and variational inference to enable self-supervised learning of probabilistic representations in a reconstruction-free, non-contrastive setting. Compared to energy-based predictive objectives that optimize pointwise discrepancies, VJE maximizes a symmetric conditional evidence lower bound (ELBO) for a latent-variable model defined directly on encoder embeddings. We instantiate the conditional likelihood with a heavy-tailed Student-$t$ model using a polar decomposition that explicitly decouples directional and radial factors to prevent norm-induced instabilities during training. VJE employs an amortized inference network to parameterize a diagonal Gaussian variational posterior whose feature-wise variances are shared with the likelihood scale to capture anisotropic uncertainty without auxiliary projection heads. Across ImageNet-1K, CIFAR-10/100, and STL-10, VJE achieves performance comparable to standard non-contrastive baselines under linear and k-NN evaluation. We further validate these probabilistic semantics through one-class CIFAR-10 anomaly detection, where likelihood-based scoring under the proposed model outperforms comparable self-supervised baselines.

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