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20162023
most citedModel-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System

273 citations · 1k across the 29 of their papers we have counts for

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Showing 2020Show all

11 papers · 1 filter

cs.LG2020★ 2 cited

Time Series Domain Adaptation via Sparse Associative Structure Alignment

Ruichu Cai, Jiawei Chen, Zijian Li +6

Domain adaptation on time series data is an important but challenging task. Most of the existing works in this area are based on the learning of the domain-invariant representation…

cs.IR2020★ 273 cited

Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System

Tianxin Wei, Fuli Feng, Jiawei Chen +3

The general aim of the recommender system is to provide personalized suggestions to users, which is opposed to suggesting popular items. However, the normal training paradigm, i.e.…

cs.LG2020★ 10 cited

On the Equivalence of Decoupled Graph Convolution Network and Label Propagation

Hande Dong, Jiawei Chen, Fuli Feng +4

The original design of Graph Convolution Network (GCN) couples feature transformation and neighborhood aggregation for node representation learning. Recently, some work shows that…

cs.IR2020★ 160 cited

Bias and Debias in Recommender System: A Survey and Future Directions

Jiawei Chen, Hande Dong, Xiang Wang +3

While recent years have witnessed a rapid growth of research papers on recommender system (RS), most of the papers focus on inventing machine learning models to better fit user beh…

eess.AS2020★ 43 cited

HiFiSinger: Towards High-Fidelity Neural Singing Voice Synthesis

Jiawei Chen, Xu Tan, Jian Luan +2

High-fidelity singing voices usually require higher sampling rate (e.g., 48kHz) to convey expression and emotion. However, higher sampling rate causes the wider frequency band and…

cs.CV2020★ 4 cited

Residual Frames with Efficient Pseudo-3D CNN for Human Action Recognition

Jiawei Chen, Jenson Hsiao, Chiu Man Ho

Human action recognition is regarded as a key cornerstone in domains such as surveillance or video understanding. Despite recent progress in the development of end-to-end solutions…

eess.IV2020★ 1 cited

MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint

Xinpeng Xie, Jiawei Chen, Yuexiang Li +3

Domain shift between medical images from multicentres is still an open question for the community, which degrades the generalization performance of deep learning models. Generative…

cs.CV2020★ 4 cited

Instance-aware Self-supervised Learning for Nuclei Segmentation

Xinpeng Xie, Jiawei Chen, Yuexiang Li +3

Due to the wide existence and large morphological variances of nuclei, accurate nuclei instance segmentation is still one of the most challenging tasks in computational pathology.…

eess.IV2020★ 11 cited

Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation

Yuexiang Li, Jiawei Chen, Xinpeng Xie +2

Witnessing the success of deep learning neural networks in natural image processing, an increasing number of studies have been proposed to develop deep-learning-based frameworks fo…

cs.CV2020

Generative Adversarial Networks for Video-to-Video Domain Adaptation

Jiawei Chen, Yuexiang Li, Kai Ma +1

Endoscopic videos from multicentres often have different imaging conditions, e.g., color and illumination, which make the models trained on one domain usually fail to generalize we…

cs.CV2020★ 4 cited

VAE/WGAN-Based Image Representation Learning For Pose-Preserving Seamless Identity Replacement In Facial Images

Hiroki Kawai, Jiawei Chen, Prakash Ishwar +1

We present a novel variational generative adversarial network (VGAN) based on Wasserstein loss to learn a latent representation from a face image that is invariant to identity but…