Variational Autoencoder Layer

• URL: http://arxiv.org/abs/2606.25900v1
• Date: Wed, 24 Jun 2026 14:48:23 GMT
• Title: Variational Autoencoder Layer
• Abstract summary: Variational Autoencoders (VAEs) belong to a family of autoencoders with probabilistic properties.<n>This paper introduces a novel approach to integrate VAEs as a neural network layer.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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