自动化学报2017，Vol.43Issue(9)：1563-1570,8.DOI:10.16383/j.aas.2017.c160643
使用增强学习训练多焦点聚焦模型
Using Reinforce Learning to Train Multi-attention Model
摘要
Abstract
Attention model (AM) concentrates computing resources on specific areas of the input data.Compared with the convolutional neural network,AM has many advantages:fewer parameters,the amount of computation being independent of the input,higher tolerance for noise input,etc.Generally,the focused area is smaller than the input image and target.However,if the focused area is too small,it will lead to more iterations and a low efficiency;besides,it is difficult to recognize multiple targets in the same input.Therefore,this paper proposes a multi-focus model.However,if on multiple focuses in parallel.This model uses reinforce learning (RL) to train,and scores the behaviors of all focuses uniformly during training.Compared with the single focus model,both the training and recognition speeds are improved by 25 ％.At the same time,the model has good generality.关键词
深度学习/聚焦模型/增强学习/多焦点Key words
Deep learning/attention model (AM)/reinforce learning (RL)/multi-attention引用本文复制引用
刘畅,刘勤让..使用增强学习训练多焦点聚焦模型[J].自动化学报,2017,43(9):1563-1570,8.基金项目
国家高技术研究发展计划(863计划)(2014AA01A),国家自然科学基金(61572520)资助 Supported by National High Technology Research and Development Program of China (863 Program) (2014AA01A) and National Natural Science Foundation of China (61572520) （863计划）
