Search Results for author: Guoren Wang

Found 18 papers, 10 papers with code

Robust Knowledge Adaptation for Dynamic Graph Neural Networks

no code implementations22 Jul 2022 Hanjie Li, Changsheng Li, Kaituo Feng, Ye Yuan, Guoren Wang, Hongyuan Zha

Recent years have witnessed the increasing attentions paid to dynamic graph neural networks for modelling such graph data, where almost all the existing approaches assume that when a new link is built, the embeddings of the neighbor nodes should be updated by learning the temporal dynamics to propagate new information.

reinforcement-learning

Multi-Prior Learning via Neural Architecture Search for Blind Face Restoration

1 code implementation28 Jun 2022 Yanjiang Yu, Puyang Zhang, Kaihao Zhang, Wenhan Luo, Changsheng Li, Ye Yuan, Guoren Wang

To this end, we propose a Face Restoration Searching Network (FRSNet) to adaptively search the suitable feature extraction architecture within our specified search space, which can directly contribute to the restoration quality.

Blind Face Restoration Neural Architecture Search

FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks

no code implementations14 Jun 2022 Kaituo Feng, Changsheng Li, Ye Yuan, Guoren Wang

Knowledge distillation (KD) has demonstrated its effectiveness to boost the performance of graph neural networks (GNNs), where its goal is to distill knowledge from a deeper teacher GNN into a shallower student GNN.

Knowledge Distillation reinforcement-learning +1

Blind Face Restoration: Benchmark Datasets and a Baseline Model

2 code implementations8 Jun 2022 Puyang Zhang, Kaihao Zhang, Wenhan Luo, Changsheng Li, Guoren Wang

To address this problem, we first synthesize two blind face restoration benchmark datasets called EDFace-Celeb-1M (BFR128) and EDFace-Celeb-150K (BFR512).

Blind Face Restoration

Self-Supervised Information Bottleneck for Deep Multi-View Subspace Clustering

no code implementations26 Apr 2022 Shiye Wang, Changsheng Li, Yanming Li, Ye Yuan, Guoren Wang

Inheriting the advantages from information bottleneck, SIB-MSC can learn a latent space for each view to capture common information among the latent representations of different views by removing superfluous information from the view itself while retaining sufficient information for the latent representations of other views.

Multi-view Subspace Clustering

SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation

1 code implementation19 Apr 2022 Binhui Xie, Shuang Li, Mingjia Li, Chi Harold Liu, Gao Huang, Guoren Wang

Specifically, to explore proper semantic concepts, we first investigate a centroid-aware pixel contrast that employs the category centroids of the entire source domain or a single source image to guide the learning of discriminative features.

Semantic Segmentation Synthetic-to-Real Translation

LegoDNN: Block-grained Scaling of Deep Neural Networks for Mobile Vision

no code implementations18 Dec 2021 Rui Han, Qinglong Zhang, Chi Harold Liu, Guoren Wang, Jian Tang, Lydia Y. Chen

The prior art sheds light on exploring the accuracy-resource tradeoff by scaling the model sizes in accordance to resource dynamics.

Knowledge Distillation Model Compression +1

Pareto Domain Adaptation

1 code implementation NeurIPS 2021 Fangrui Lv, Jian Liang, Kaixiong Gong, Shuang Li, Chi Harold Liu, Han Li, Di Liu, Guoren Wang

Domain adaptation (DA) attempts to transfer the knowledge from a labeled source domain to an unlabeled target domain that follows different distribution from the source.

Domain Adaptation Image Classification +1

Active Learning for Domain Adaptation: An Energy-Based Approach

1 code implementation2 Dec 2021 Binhui Xie, Longhui Yuan, Shuang Li, Chi Harold Liu, Xinjing Cheng, Guoren Wang

Unsupervised domain adaptation has recently emerged as an effective paradigm for generalizing deep neural networks to new target domains.

Active Learning Unsupervised Domain Adaptation

Deep Unsupervised Active Learning on Learnable Graphs

no code implementations8 Nov 2021 Handong Ma, Changsheng Li, Xinchu Shi, Ye Yuan, Guoren Wang

To make the learnt graph structure more stable and effective, we take into account $k$-nearest neighbor graph as a priori, and learn a relation propagation graph structure.

Active Learning Graph structure learning +1

Causal Effect Estimation using Variational Information Bottleneck

1 code implementation26 Oct 2021 Zhenyu Lu, Yurong Cheng, Mingjun Zhong, George Stoian, Ye Yuan, Guoren Wang

A typical approach is to formulate causal inference as a supervised learning problem and so counterfactual could be predicted.

Causal Inference

Semantic Distribution-aware Contrastive Adaptation for Semantic Segmentation

1 code implementation11 May 2021 Shuang Li, Binhui Xie, Bin Zang, Chi Harold Liu, Xinjing Cheng, Ruigang Yang, Guoren Wang

Specifically, we first design a pixel-wise contrastive loss by considering the correspondences between semantic distributions and pixel-wise representations from both domains.

Self-Supervised Learning Semantic Segmentation

Generalized Domain Conditioned Adaptation Network

1 code implementation23 Mar 2021 Shuang Li, Binhui Xie, Qiuxia Lin, Chi Harold Liu, Gao Huang, Guoren Wang

Domain Adaptation (DA) attempts to transfer knowledge learned in the labeled source domain to the unlabeled but related target domain without requiring large amounts of target supervision.

Domain Adaptation

On Deep Unsupervised Active Learning

no code implementations28 Jul 2020 Changsheng Li, Handong Ma, Zhao Kang, Ye Yuan, Xiao-Yu Zhang, Guoren Wang

Unsupervised active learning has attracted increasing attention in recent years, where its goal is to select representative samples in an unsupervised setting for human annotating.

Active Learning

Efficiently Indexing Large Sparse Graphs for Similarity Search

no code implementations18 Feb 2010 Guoren Wang, Bin Wang, Xiaochun Yang, IEEE Computer Society, and Ge Yu, Member, IEEE

Abstract—The graph structure is a very important means to model schemaless data with complicated structures, such as protein- protein interaction networks, chemical compounds, knowledge query inferring systems, and road networks.

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