The paper introduces a novel approach called Single Path One-Shot (SPOS) for Neural Architecture Search (NAS). SPOS decouples architecture search from supernet training by using a simplified supernet with single paths and a uniform path sampling strategy, significantly improving efficiency and effectiveness. The method also incorporates channel search and mixed-precision quantization, leading to the discovery of accurate and resource-efficient neural network architectures.SPOS addresses limitations of existing NAS methods by simplifying the supernet structure, utilizing an evolutionary algorithm, and incorporating channel search and mixed-precision quantization. The approach outperforms previous methods in accuracy, complexity, and resource efficiency. It demonstrates strong correlation between supernet and individual architecture performance, enhancing the search process efficiency.Read full paper: https://arxiv.org/abs/1904.00420Tags: Deep Learning, Optimization, Machine Learning