残差神经网络
残余物
计算机科学
架空(工程)
建筑
国家(计算机科学)
对偶(语法数字)
并行计算
人工智能
计算机工程
算法
程序设计语言
艺术
文学类
视觉艺术
作者
Sasha Targ,Diogo Almeida,Kevin Lyman
出处
期刊:Cornell University - arXiv
日期:2016-01-01
被引量:601
标识
DOI:10.48550/arxiv.1603.08029
摘要
Residual networks (ResNets) have recently achieved state-of-the-art on challenging computer vision tasks. We introduce Resnet in Resnet (RiR): a deep dual-stream architecture that generalizes ResNets and standard CNNs and is easily implemented with no computational overhead. RiR consistently improves performance over ResNets, outperforms architectures with similar amounts of augmentation on CIFAR-10, and establishes a new state-of-the-art on CIFAR-100.
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