Variational Autoencoder Layer

Gananath R

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arXiv · 2606.25900

Variational Autoencoder Layer

Gananath R

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Abstract

Variational Autoencoders (VAEs) belong to a family of autoencoders with probabilistic properties, making them well suited for generating data by producing a smooth and continuous latent space. Despite being introduced over a decade ago, the method continues to be widely adopted in both research and industry for diverse applications. While VAEs are typically used as standalone models, this paper introduces a novel approach to integrate them as a neural network layer. Furthermore, a new training strategy is proposed for models incorporating these layers, and their performance is thoroughly analyzed.

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Gananath R. 2026-06-24. Variational Autoencoder Layer. https://arxiv.org/abs/2606.25900

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