在本文中,作者提出了一种专为需要低延迟操作的任务创建的新的深度神经网络结构——ENet (efficient neural network)。ENet最快可达18倍,需要更少的FLOPs,更少的参数,并提供与现有模型相似或更好的精度 为了同时对图像进行空间分类和精细分割,已经提出了几种神经网络结构,如SegNet或完全卷积网络,所有这些工作都基于...
A Deep Neural Network’s Loss Surface Contains Every Low-dimensional Pattern 概 作者关于Loss Surface的情况做了一个理论分析, 即证明足够大的神经网络能够逼近所有的低维损失patterns. 相关工作 loss l
Reference [1] Y. LeCun and Y. Bengio, “Convolutional networks for images, speech, and time series,” The handbook of brain theory and neural networks, pp. 255–258, 1998. [2] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural ...
ICML 2019: Yonatan Geifman, and Ran El-Yaniv, SelectiveNet: A Deep Neural Network with an Integrated Reject Option 在一些分类问题中,如果遇到在训练阶段未标注的目标还要进行分类么?如果对应于各类的分数相近,要直接将其分到分数最高的一类么?针对这样的问题,有一种方法叫做选择性预测 (Selective Prediction...
A deep neural network is a neural network with a certain level of complexity, a neural network with more than two layers. Deep neural networks use sophisticated mathematical modeling to process data in complex ways. Advertisements Techopedia Explains Deep Neural Network A neural network, in general...
深度学习论文: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation及其PyTorch实现 PyTorch: https://github.com/shanglianlm0525/PyTorch-Networks 1 概述 ENet是16年初的一篇工作了,能够达到实时的语义分割,包括在嵌入式设备NVIDIA TX1,同时还能够保证网络的效果。
全像素分割要求输出具有与输入相同的分辨率。 这意味着强大的下采样将需要同样强大的上采样,这增加了模型尺寸和计算成本 针对问题1,有两个解决方案: FCN的解决办法是将encoder阶段的feature map塞给decoder,增加空间信息。 SegNet的解决办法是将encoder阶段做downsampling的indices保留到decoder阶段...
A recurrent neural network or RNN is a deepneural networktrained on sequential or time series data to create amachine learning(ML) model that can make sequential predictions or conclusions based on sequential inputs. An RNN might be used to predict daily flood levels based on past daily flood...
The sparsity-driven technique is a widely used tool to solve the synthetic aperture radar (SAR) imaging problem. However, it always encounters sensitivity to motion errors. To solve this problem, this article proposes a new deep neural network architecture, i.e., the sparse autoencoder network ...
名称:A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data 出处:AAAI19 下载地址:https://arxiv.org/abs/1811.08055 原作者代码:https://github.com/7fantasysz/MSCRED 2论文介绍 2.0研究背景 时间序列异常检测发现,一般是解决一个这样的问题:对于一串时序数据,...