Search Results for author: Xiaoguang Wang

Found 6 papers, 2 papers with code

Line Drawing Guided Progressive Inpainting of Mural Damages

1 code implementation12 Nov 2022 Luxi Li, Qin Zou, Fan Zhang, Hongkai Yu, Long Chen, Chengfang Song, Xianfeng Huang, Xiaoguang Wang

Mural image inpainting refers to repairing the damage or missing areas in a mural image to restore the visual appearance.

Image Inpainting

Label prompt for multi-label text classification

no code implementations18 Jun 2021 Rui Song, Xingbing Chen, Zelong Liu, Haining An, Zhiqi Zhang, Xiaoguang Wang, Hao Xu

In this paper, we propose a Label Mask multi-label text classification model (LM-MTC), which is inspired by the idea of cloze questions of language model.

Language Modelling Multi Label Text Classification +2

Quantum Fisher information matrix and multiparameter estimation

no code implementations18 Jul 2019 Jing Liu, Haidong Yuan, Xiao-Ming Lu, Xiaoguang Wang

Quantum Fisher information matrix (QFIM) is a core concept in theoretical quantum metrology due to the significant importance of quantum Cram\'{e}r-Rao bound in quantum parameter estimation.

Quantum Physics

CUTIE: Learning to Understand Documents with Convolutional Universal Text Information Extractor

4 code implementations29 Mar 2019 Xiaohui Zhao, Endi Niu, Zhuo Wu, Xiaoguang Wang

Extracting key information from documents, such as receipts or invoices, and preserving the interested texts to structured data is crucial in the document-intensive streamline processes of office automation in areas that includes but not limited to accounting, financial, and taxation areas.

Named Entity Recognition

Improving the Interpretability of Deep Neural Networks with Knowledge Distillation

no code implementations28 Dec 2018 Xuan Liu, Xiaoguang Wang, Stan Matwin

To tackle this problem, we apply the Knowledge Distillation technique to distill Deep Neural Networks into decision trees in order to attain good performance and interpretability simultaneously.

Ethics Knowledge Distillation +3

Interpretable Deep Convolutional Neural Networks via Meta-learning

no code implementations2 Feb 2018 Xuan Liu, Xiaoguang Wang, Stan Matwin

We attempt to address this challenge by proposing a technique called CNN-INTE to interpret deep Convolutional Neural Networks (CNN) via meta-learning.

Clustering Fairness +1

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