Search Results for author: Minghua Deng

Found 11 papers, 4 papers with code

TGNN: A Joint Semi-supervised Framework for Graph-level Classification

no code implementations23 Apr 2023 Wei Ju, Xiao Luo, Meng Qu, Yifan Wang, Chong Chen, Minghua Deng, Xian-Sheng Hua, Ming Zhang

The two twin modules collaborate with each other by exchanging instance similarity knowledge to fully explore the structure information of both labeled and unlabeled data.

Graph Classification

Geometric Anchor Correspondence Mining With Uncertainty Modeling for Universal Domain Adaptation

no code implementations CVPR 2022 Liang Chen, Yihang Lou, Jianzhong He, Tao Bai, Minghua Deng

Therefore, in this paper, we propose a Geometric anchor-guided Adversarial and conTrastive learning framework with uncErtainty modeling called GATE to alleviate these issues.

Contrastive Learning Universal Domain Adaptation

Neighborhood Consensus Contrastive Learning for Backward-Compatible Representation

no code implementations7 Aug 2021 Shengsen Wu, Liang Chen, Yihang Lou, Yan Bai, Tao Bai, Minghua Deng, Lingyu Duan

Therefore, backward-compatible representation is proposed to enable "new" features to be compared with "old" features directly, which means that the database is active when there are both "new" and "old" features in it.

Contrastive Learning

DNA-GCN: Graph convolutional networks for predicting DNA-protein binding

1 code implementation2 Jun 2021 Yuhang Guo, Xiao Luo, Liang Chen, Minghua Deng

Predicting DNA-protein binding is an important and classic problem in bioinformatics.


Criterion-based Heterogeneous Collaborative Filtering for Multi-behavior Implicit Recommendation

1 code implementation25 May 2021 Xiao Luo, Daqing Wu, Yiyang Gu, Chong Chen, Luchen Liu, Jinwen Ma, Ming Zhang, Minghua Deng, Jianqiang Huang, Xian-Sheng Hua

Besides, CHCF integrates criterion learning and user preference learning into a unified framework, which can be trained jointly for the interaction prediction of the target behavior.

Collaborative Filtering Metric Learning +1

Deep Unsupervised Hashing by Distilled Smooth Guidance

no code implementations13 May 2021 Xiao Luo, Zeyu Ma, Daqing Wu, Huasong Zhong, Chong Chen, Jinwen Ma, Minghua Deng

Hashing has been widely used in approximate nearest neighbor search for its storage and computational efficiency.

Clustering Computational Efficiency +1

Graph Contrastive Clustering

1 code implementation ICCV 2021 Huasong Zhong, Jianlong Wu, Chong Chen, Jianqiang Huang, Minghua Deng, Liqiang Nie, Zhouchen Lin, Xian-Sheng Hua

On the other hand, a novel graph-based contrastive learning strategy is proposed to learn more compact clustering assignments.

Clustering Contrastive Learning

CIMON: Towards High-quality Hash Codes

no code implementations15 Oct 2020 Xiao Luo, Daqing Wu, Zeyu Ma, Chong Chen, Minghua Deng, Jinwen Ma, Zhongming Jin, Jianqiang Huang, Xian-Sheng Hua

However, due to the inefficient representation ability of the pre-trained model, many false positives and negatives in local semantic similarity will be introduced and lead to error propagation during the hash code learning.

Computational Efficiency Image Augmentation +4

A Survey on Deep Hashing Methods

no code implementations4 Mar 2020 Xiao Luo, Haixin Wang, Daqing Wu, Chong Chen, Minghua Deng, Jianqiang Huang, Xian-Sheng Hua

Nearest neighbor search aims to obtain the samples in the database with the smallest distances from them to the queries, which is a basic task in a range of fields, including computer vision and data mining.

Deep Hashing Domain Adaptation +4

Predicting protein inter-residue contacts using composite likelihood maximization and deep learning

1 code implementation31 Aug 2018 Haicang Zhang, Qi Zhang, Fusong Ju, Jianwei Zhu, Yujuan Gao, Ziwei Xie, Minghua Deng, Shiwei Sun, Wei-Mou Zheng, Dongbo Bu

We further present successful application of the predicted contacts to accurately build tertiary structures for proteins in the PSICOV dataset.

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