Long Short-Term Memory Spatial Transformer Network

被引：0
|
作者：
Feng, Shiyang [1 ]
Chen, Tianyue [2 ]
Sun, Hao [1 ]
机构：
[1] Donghua Univ, Coll Informat Sci & Technol, Shanghai, Peoples R China
[2] Donghua Univ, Coll Sci, Shanghai, Peoples R China
来源：
PROCEEDINGS OF 2019 IEEE 8TH JOINT INTERNATIONAL INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE CONFERENCE (ITAIC 2019) | 2019年
关键词：
LSTM; STN; CNN; Top-down attention mechanism; ATTENTION; SALIENT; LSTM;
D O I：
10.1109/itaic.2019.8785574
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Spatial transformer network has been used in a layered form in conjunction with a convolutional network to enable the model to transform data spatially. In this paper, we propose a combined spatial transformer network (STN) and a Long Short-Term Memory network (LSTM) to classify digits in sequences formed by MINST elements. This LSTM-STN model has a top-down attention mechanism profit from LSTM layer, so that the STN layer can perform short-term independent elements for the statement in the process of spatial transformation, thus avoiding the distortion that may be caused when the entire sequence is spatially transformed. It also avoids the influence of this distortion on the subsequent classification process using convolutional neural networks and achieves a single digit error of 1.6% compared with 2.2% of Convolutional Neural Network with STN layer.
引用
收藏
页码：239 / 242
页数：4
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