Virtual neural networks: hundreds of souls in a body

2026-09-21

AI Summary

提出虚拟神经网络概念，通过计算资源实现扩展性而不增加参数量，结合孪生网络与深度集成技术提高模型鲁棒性和准确性。

Abstract

A new concept, termed virtual neural networks, is introduced, where the count of trainable parameters is kept constant, and scalability is attained purely through computational resources. This concept is an abstract framework that can be realized using any standard convolutional neural network. It merges siamese neural networks with a deep ensemble technique by generating numerous virtual models that share weights derived from a small set of physical models. The ensemble comprises up to hundreds of trained models simultaneously. All virtual networks take the same input, and their interconnected structure induces an internal distortion that boosts the entire ensemble robustness. The accuracy of the ensemble improves as the number of virtual networks increases, without changing the capacity. Virtual neural networks outperform larger capacity models, typical deep ensembles, and contemporary approaches like SWA and Masksembles. Additionally, the highest-performing individual model from the ensemble surpasses other models trained individually, even those with a greater number of parameters. Code: gitlab.com/EnginCZ/virtual-models-public

Problem

• virtual neural networks
• scalability
• computational resources
• robustness
• accuracy

Innovation

• virtual neural networks
• scalability through computational resources
• ensemble robustness
• shared weights
• performance improvement