CASIA OpenIR  > 模式识别国家重点实验室  > 模式分析与学习
Image-to-Markup Generation via Paired Adversarial Learning
Jin-Wen Wu; Fei Yin; Yan-Ming Zhang; Xu-Yao Zhang; Cheng-Lin Liu
Conference NameEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2018
Conference Date10-14
Conference PlaceDublin, Ireland
AbstractMotivated by the fact that humans can grasp semantic-invariant features shared by the same category while attention-based models focus mainly on discriminative features of each object, we propose a scalable paired adversarial learning (PAL) method for image-to-markup generation. PAL can incorporate the prior knowledge of standard templates to guide the attention-based model for discovering semantic-invariant features when the model pays attention to regions of interest. Furthermore, we also extend the convolutional attention mechanism to speed up the image-to-markup parsing process while achieving competitive performance compared with recurrent attention models. We evaluate the proposed method in the scenario of handwritten-image-to-LaTeX generation, i.e., converting handwritten mathematical expressions to LaTeX. Experimental results show that our method can significantly improve the generalization performance over standard attention-based encoder-decoder models.
Document Type会议论文
AffiliationNLPR, Institute of Automation, Chinese Academy of Sciences
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Jin-Wen Wu,Fei Yin,Yan-Ming Zhang,et al. Image-to-Markup Generation via Paired Adversarial Learning[C],2018.
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