.png){kind=icon}
返回
Constructing Deep Sparse Coding Network for image classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2016.10.032.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper introduces a deep model called Deep Sparse-Coding Network (DeepSCNet) to combine the advantages of Convolutional Neural Network (CNN) and sparse-coding techniques for image feature representation. DeepSCNet consists of four type of basic layers: The sparse-coding layer performs generalized linear coding for local patch within the receptive field by replacing the convolution operation in CNN into sparse-coding. The Pooling layer and the Normalization layer perform identical operations as that in CNN. And finally the Map reduction layer reduces CPU/memory consumption by reducing the number of feature maps before stacking with the following layers. These four type of layers can be easily stacked to construct a deep model for image feature learning. The paper further discusses the multi-scale, multi-locality extension to the basic DeepSCNet, and the overall approach is fully unsupervised. Compared to CNN, training DeepSCNet is relatively easier even with training set of moderate size. Experiments show that DeepSCNet can automatically discover highly discriminative feature directly from raw image pixels.
Keyword:
Sparse Coding
Deep Model
Multi-scale
Multi-locality
Image classification
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
MICAL2is a novel human cancer gene controlling mesenchymal to epithelial transition involved in cancer growth and invasion.png)
Oncotarget
OAAI
OncotargetIF0
OAAI分享收藏Crystalline‐State Reaction with Allosteric Effect in Spin‐Crossover, Interpenetrated Networks with Magnetic and Optical Bistability具有磁和光学双稳态的自旋交叉，互穿网络中具有变构效应的晶态反应
PREAI
分享收藏
A micromachined efficient parametric array loudspeaker with a wide radiation frequency band具有宽辐射频带的微机械高效参量阵列扬声器
PREAI
分享收藏分享收藏
查看更多内容