Search Results for author: Jingyang Lin

Found 7 papers, 3 papers with code

Predicting Adverse Neonatal Outcomes for Preterm Neonates with Multi-Task Learning

no code implementations28 Mar 2023 Jingyang Lin, Junyu Chen, Hanjia Lyu, Igor Khodak, Divya Chhabra, Colby L Day Richardson, Irina Prelipcean, Andrew M Dylag, Jiebo Luo

In this work, we first analyze the correlations between three adverse neonatal outcomes and then formulate the diagnosis of multiple neonatal outcomes as a multi-task learning (MTL) problem.

Feature Importance Multi-Task Learning

Out-of-Distribution Detection with Hilbert-Schmidt Independence Optimization

1 code implementation26 Sep 2022 Jingyang Lin, Yu Wang, Qi Cai, Yingwei Pan, Ting Yao, Hongyang Chao, Tao Mei

Existing works attempt to solve the problem by explicitly imposing uncertainty on classifiers when OOD inputs are exposed to the classifier during training.

Outlier Detection Out-of-Distribution Detection +1

CORE-Text: Improving Scene Text Detection with Contrastive Relational Reasoning

1 code implementation14 Dec 2021 Jingyang Lin, Yingwei Pan, Rongfeng Lai, Xuehang Yang, Hongyang Chao, Ting Yao

In this work, we quantitatively analyze the sub-text problem and present a simple yet effective design, COntrastive RElation (CORE) module, to mitigate that issue.

Relational Reasoning Scene Text Detection +1

A Low Rank Promoting Prior for Unsupervised Contrastive Learning

no code implementations5 Aug 2021 Yu Wang, Jingyang Lin, Qi Cai, Yingwei Pan, Ting Yao, Hongyang Chao, Tao Mei

In this paper, we construct a novel probabilistic graphical model that effectively incorporates the low rank promoting prior into the framework of contrastive learning, referred to as LORAC.

Contrastive Learning Image Classification +5

Improving Fast Segmentation With Teacher-student Learning

no code implementations19 Oct 2018 Jiafeng Xie, Bing Shuai, Jian-Fang Hu, Jingyang Lin, Wei-Shi Zheng

Recently, segmentation neural networks have been significantly improved by demonstrating very promising accuracies on public benchmarks.

Cannot find the paper you are looking for? You can Submit a new open access paper.