计算机科学
人工神经网络
分割
深层神经网络
失败
建筑
人工智能
可用性
深度学习
网络体系结构
延迟(音频)
实时计算
并行计算
电信
人机交互
艺术
视觉艺术
计算机安全
作者
Adam Paszke,Abhishek Chaurasia,Sangpil Kim,Eugenio Culurciello
出处
期刊:Cornell University - arXiv
日期:2016-06-07
被引量:341
摘要
The ability to perform pixel-wise semantic segmentation in real-time is of paramount importance in mobile applications. Recent deep neural networks aimed at this task have the disadvantage of requiring a large number of floating point operations and have long run-times that hinder their usability. In this paper, we propose a novel deep neural network architecture named ENet (efficient neural network), created specifically for tasks requiring low latency operation. ENet is up to 18$\times$ faster, requires 75$\times$ less FLOPs, has 79$\times$ less parameters, and provides similar or better accuracy to existing models. We have tested it on CamVid, Cityscapes and SUN datasets and report on comparisons with existing state-of-the-art methods, and the trade-offs between accuracy and processing time of a network. We present performance measurements of the proposed architecture on embedded systems and suggest possible software improvements that could make ENet even faster.
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