中国科学院自动化研究所机构知识库

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.