Knowledge Commons of Institute of Automation,CAS
Deep Transfer Mapping for Unsupervised Writer Adaptation | |
Hong-Ming Yang1,2; Xu-Yao Zhang1,2; Fei Yin1,2; Jun Sun4; Cheng-Lin Liu1,2,3 | |
2018 | |
会议名称 | International Conference on Frontiers in Handwriting Recognition |
会议日期 | 2018-08-05 |
会议地点 | Niagara Falls, USA |
摘要 | Convolutional neural network (CNN) has achieved great success in handwriting recognition. However, it relies on large set of labeled data in training and its performance will deteriorate when the data distribution varies. To solve this problem, traditional methods usually consider adaptation of the single top layer of CNN. To better reduce the distribution discrepancy, in this paper, we consider adaptation of all layers of CNN including both convolutional and full layers. Four variations of transformations are designed based on different assumptions about the space relations for adaptation of convolutional layers. In order to make adaptation of multiple layers, we propose to cascade the transformations of different layers to conduct adaptation in a deep manner, and therefore this method is denoted as deep transfer mapping (DTM). DTM can capture the information from different layers and minimize the data divergence under different information abstract levels, thus it is more powerful and flexible for domain adaptation. Experiments on the online Chinese handwriting dataset (OLHWDB) demonstrate the efficiency and effectiveness of the proposed method for unsupervised writer adaptation. |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44418 |
专题 | 多模态人工智能系统全国重点实验室_模式分析与学习 |
通讯作者 | Cheng-Lin Liu |
作者单位 | 1.NLPR, Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences 3.CAS Center for Excellence of Brain Science and Intelligence Technology 4.Fujitsu Research & Development Center |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Hong-Ming Yang,Xu-Yao Zhang,Fei Yin,et al. Deep Transfer Mapping for Unsupervised Writer Adaptation[C],2018. |
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Deep Transfer Mappin(164KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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