虚拟神经网络：一个身体中的数百个灵魂
Virtual neural networks: hundreds of souls in a body
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中文总结 AI 辅助
提出虚拟神经网络概念，保持可训练参数不变，仅通过计算资源扩展，生成大量共享权重的虚拟模型组成深度集成，提升鲁棒性和准确性，优于更大容量模型及现有方法。
中文摘要 AI 辅助
本文提出了一种新概念，称为虚拟神经网络，其中可训练参数的数量保持不变，而可扩展性仅通过计算资源来实现。该概念是一个抽象框架，可使用任何标准卷积神经网络实现。它通过生成大量共享权重的虚拟模型，将孪生神经网络与深度集成技术相结合，这些权重源自少量物理模型。该集成同时包含多达数百个训练好的模型。所有虚拟网络接收相同的输入，其互连结构会引发内部扭曲，从而增强整个集成的鲁棒性。集成的准确性随着虚拟网络数量的增加而提高，而无需改变容量。虚拟神经网络在性能上优于更大容量的模型、典型的深度集成以及诸如SWA和Masksembles等当代方法。此外，集成中表现最佳的单个模型也超越了其他单独训练的模型，即使这些模型拥有更多参数。代码：此HTTP URL。
英文摘要
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
发表机构
- Centre of Excellence IT4Innovations, Institute for Research and Applications of Fuzzy Modeling（卓越中心IT4Innovations，模糊建模研究与应用研究所）
- University of Ostrava（俄斯特拉发大学）
机构由 AI 辅助整理，请以论文原文为准。
