🤖 AI Summary
This work addresses the challenge of integrating variational autoencoders (VAEs) as trainable layers within neural networks. It proposes a general framework for flexibly embedding VAEs into arbitrary network architectures, accompanied by an end-to-end training strategy that leverages the reparameterization trick and probabilistic modeling to ensure full differentiability throughout the pipeline. For the first time, this approach enables VAEs to function as plug-and-play modules akin to standard neural network layers, substantially enhancing their compatibility and representational capacity within complex models. Experimental results demonstrate that the proposed VAE layer consistently achieves stable performance across diverse tasks and outperforms conventional standalone VAE models, thereby significantly expanding the applicability of VAEs in deep learning systems.
📝 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.