Search Results for author: Ziyang Liu

Found 14 papers, 3 papers with code

Graph Contrastive Learning with Generative Adversarial Network

no code implementations1 Aug 2023 Cheng Wu, Chaokun Wang, Jingcao Xu, Ziyang Liu, Kai Zheng, Xiaowei Wang, Yang song, Kun Gai

Specifically, we present GACN, a novel Generative Adversarial Contrastive learning Network for graph representation learning.

Contrastive Learning Data Augmentation +2

PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation

no code implementations7 Jun 2023 Ziyang Liu, Chaokun Wang, Jingcao Xu, Cheng Wu, Kai Zheng, Yang song, Na Mou, Kun Gai

Recommender systems play a crucial role in addressing the issue of information overload by delivering personalized recommendations to users.

Denoising Graph Representation Learning +1

Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Search

no code implementations4 Oct 2022 Ziyang Liu, Chaokun Wang, Hao Feng, Lingfei Wu, Liqun Yang

In this paper, we design an efficient knowledge distillation framework for e-commerce relevance matching to integrate the respective advantages of Transformer-style models and classical relevance matching models.

Knowledge Distillation

TeKo: Text-Rich Graph Neural Networks with External Knowledge

no code implementations15 Jun 2022 Zhizhi Yu, Di Jin, Jianguo Wei, Ziyang Liu, Yue Shang, Yun Xiao, Jiawei Han, Lingfei Wu

Graph Neural Networks (GNNs) have gained great popularity in tackling various analytical tasks on graph-structured data (i. e., networks).

RDU: A Region-based Approach to Form-style Document Understanding

no code implementations14 Jun 2022 Fengbin Zhu, Chao Wang, Wenqiang Lei, Ziyang Liu, Tat Seng Chua

Key Information Extraction (KIE) is aimed at extracting structured information (e. g. key-value pairs) from form-style documents (e. g. invoices), which makes an important step towards intelligent document understanding.

document understanding Key Information Extraction +5

DSRGAN: Detail Prior-Assisted Perceptual Single Image Super-Resolution via Generative Adversarial Networks

no code implementations25 Dec 2021 Ziyang Liu, Zhengguo Li, Xingming Wu, Zhong Liu, Weihai Chen

The proposed method, named DSRGAN, includes a well designed detail extraction algorithm to capture the most important high frequency information from images.

Image Super-Resolution

FAMINet: Learning Real-time Semi-supervised Video Object Segmentation with Steepest Optimized Optical Flow

1 code implementation20 Nov 2021 Ziyang Liu, Jingmeng Liu, Weihai Chen, Xingming Wu, Zhengguo Li

A FAMINet, which consists of a feature extraction network (F), an appearance network (A), a motion network (M), and an integration network (I), is proposed in this study to address the abovementioned problem.

Optical Flow Estimation Segmentation +3

Deep Joint Demosaicing and High Dynamic Range Imaging within a Single Shot

no code implementations14 Nov 2021 Yilun Xu, Ziyang Liu, Xingming Wu, Weihai Chen, Changyun Wen, Zhengguo Li

For the former challenge, a spatially varying convolution (SVC) is designed to process the Bayer images carried with varying exposures.


Rethinking Temperature in Graph Contrastive Learning

no code implementations29 Sep 2021 Ziyang Liu, Hao Feng, Chaokun Wang

In this paper, we investigate and discuss what a good representation should be for a general loss (InfoNCE) in graph contrastive learning.

Contrastive Learning Self-Supervised Learning

Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss

1 code implementation6 Jun 2021 Jian Cheng, Ziyang Liu, Hao Guan, Zhenzhou Wu, Haogang Zhu, Jiyang Jiang, Wei Wen, DaCheng Tao, Tao Liu

In this paper, a novel 3D convolutional network, called two-stage-age-network (TSAN), is proposed to estimate brain age from T1-weighted MRI data.

Age Estimation

Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search

no code implementations13 Jan 2021 Ziyang Liu, Zhaomeng Cheng, Yunjiang Jiang, Yue Shang, Wei Xiong, Sulong Xu, Bo Long, Di Jin

We propose in this paper a novel Second-order Relevance, which is fundamentally different from the previous First-order Relevance, to improve result relevance prediction.

Network Embedding

BiTe-GCN: A New GCN Architecture via BidirectionalConvolution of Topology and Features on Text-Rich Networks

no code implementations23 Oct 2020 Di Jin, Xiangchen Song, Zhizhi Yu, Ziyang Liu, Heling Zhang, Zhaomeng Cheng, Jiawei Han

We propose BiTe-GCN, a novel GCN architecture with bidirectional convolution of both topology and features on text-rich networks to solve these limitations.

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