Variational Self-Supervised Learning

• URL: http://arxiv.org/abs/2504.04318v3
• Date: Thu, 01 May 2025 16:21:49 GMT
• Title: Variational Self-Supervised Learning
• Authors: Mehmet Can Yavuz, Berrin Yanikoglu,
• Abstract summary: We present Variational Self-Supervised Learning (VSSL), a novel framework that combines variational inference with self-supervised learning.<n>A momentum-updated teacher network defines a dynamic, data-dependent prior, while the student encoder produces an approximate posterior from augmented views.<n> Experiments on CIFAR-10, CIFAR-100, and ImageNet-100 show that VSSL achieves competitive or superior performance to leading self-supervised methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Variational Self-Supervised Learning (VSSL), a novel framework that combines variational inference with self-supervised learning to enable efficient, decoder-free representation learning. Unlike traditional VAEs that rely on input reconstruction via a decoder, VSSL symmetrically couples two encoders with Gaussian outputs. A momentum-updated teacher network defines a dynamic, data-dependent prior, while the student encoder produces an approximate posterior from augmented views. The reconstruction term in the ELBO is replaced with a cross-view denoising objective, preserving the analytical tractability of Gaussian KL divergence. We further introduce cosine-based formulations of KL and log-likelihood terms to enhance semantic alignment in high-dimensional latent spaces. Experiments on CIFAR-10, CIFAR-100, and ImageNet-100 show that VSSL achieves competitive or superior performance to leading self-supervised methods, including BYOL and MoCo V3. VSSL offers a scalable, probabilistically grounded approach to learning transferable representations without generative reconstruction, bridging the gap between variational modeling and modern self-supervised techniques.

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