1 code implementation • 7 Jun 2024 • Feng Hong, Yueming Lyu, Jiangchao Yao, Ya zhang, Ivor W. Tsang, Yanfeng Wang
The remarkable success of modern machine learning models on large datasets often demands extensive training time and resource consumption.
1 code implementation • 18 Dec 2023 • Tianjie Dai, Ruipeng Zhang, Feng Hong, Jiangchao Yao, Ya zhang, Yanfeng Wang
Vision-Language Pre-training (VLP) that utilizes the multi-modal information to promote the training efficiency and effectiveness, has achieved great success in vision recognition of natural domains and shown promise in medical imaging diagnosis for the Chest X-Rays (CXRs).
1 code implementation • NeurIPS 2023 • Zhihan Zhou, Jiangchao Yao, Feng Hong, Ya zhang, Bo Han, Yanfeng Wang
Self-supervised learning (SSL) as an effective paradigm of representation learning has achieved tremendous success on various curated datasets in diverse scenarios.
no code implementations • 24 Oct 2023 • Gregory Holste, Yiliang Zhou, Song Wang, Ajay Jaiswal, Mingquan Lin, Sherry Zhuge, Yuzhe Yang, Dongkyun Kim, Trong-Hieu Nguyen-Mau, Minh-Triet Tran, Jaehyup Jeong, Wongi Park, Jongbin Ryu, Feng Hong, Arsh Verma, Yosuke Yamagishi, Changhyun Kim, Hyeryeong Seo, Myungjoo Kang, Leo Anthony Celi, Zhiyong Lu, Ronald M. Summers, George Shih, Zhangyang Wang, Yifan Peng
Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" $\unicode{x2013}$ there are a few common findings followed by many more relatively rare conditions.
no code implementations • 17 Aug 2023 • Feng Hong, Tianjie Dai, Jiangchao Yao, Ya zhang, Yanfeng Wang
Clinical classification of chest radiography is particularly challenging for standard machine learning algorithms due to its inherent long-tailed and multi-label nature.
1 code implementation • 3 Aug 2023 • YuHang Zhou, Jiangchao Yao, Feng Hong, Ya zhang, Yanfeng Wang
By dynamically manipulating the gradient during training based on these factors, BDR can effectively alleviate knowledge destruction and improve knowledge reconstruction.
no code implementations • 28 Mar 2023 • Haiquan Mao, Feng Hong, Man-Wai Mak
Inspired by the self-training strategies that use an existing classifier to label the unlabeled data for retraining, we propose a cluster-guided UDA framework that labels the target domain data by clustering and combines the labeled source domain data and pseudo-labeled target domain data to train a speaker embedding network.
1 code implementation • 10 Feb 2023 • Feng Hong, Jiangchao Yao, Zhihan Zhou, Ya zhang, Yanfeng Wang
The straightforward combination of LT and PLL, i. e., LT-PLL, suffers from a fundamental dilemma: LT methods build upon a given class distribution that is unavailable in PLL, and the performance of PLL is severely influenced in long-tailed context.
no code implementations • SEMEVAL 2020 • Weilong Chen, Yan Zhuang, Peng Wang, Feng Hong, Yan Wang, Yanru Zhang
The main purpose of this article is to state the effect of using different methods and models for counterfactual determination and detection of causal knowledge.
no code implementations • 18 Aug 2020 • Zhenge Jia, Zhepeng Wang, Feng Hong, Lichuan Ping, Yiyu Shi, Jingtong Hu
We equip the system with real-time inference on both intracardiac and surface rhythm monitors.