Search Results for author: Huimin Wu

Found 8 papers, 5 papers with code

Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation

2 code implementations15 Apr 2023 Huimin Wu, Xiaomeng Li, Yiqun Lin, Kwang-Ting Cheng

This study investigates barely-supervised medical image segmentation where only few labeled data, i. e., single-digit cases are available.

Image Segmentation Pancreas Segmentation +3

Randomized Quantization: A Generic Augmentation for Data Agnostic Self-supervised Learning

1 code implementation ICCV 2023 Huimin Wu, Chenyang Lei, Xiao Sun, Peng-Shuai Wang, Qifeng Chen, Kwang-Ting Cheng, Stephen Lin, Zhirong Wu

Self-supervised representation learning follows a paradigm of withholding some part of the data and tasking the network to predict it from the remaining part.

Data Augmentation Quantization +2

Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity

no code implementations11 Oct 2022 William de Vazelhes, Hualin Zhang, Huimin Wu, Xiao-Tong Yuan, Bin Gu

To solve this puzzle, in this paper, we focus on the $\ell_0$ constrained black-box stochastic optimization problems, and propose a new stochastic zeroth-order gradient hard-thresholding (SZOHT) algorithm with a general ZO gradient estimator powered by a novel random support sampling.

Portfolio Optimization Sparse Learning +1

FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation

1 code implementation4 May 2022 Jeffry Wicaksana, Zengqiang Yan, Dong Zhang, Xijie Huang, Huimin Wu, Xin Yang, Kwang-Ting Cheng

To relax this assumption, in this work, we propose a label-agnostic unified federated learning framework, named FedMix, for medical image segmentation based on mixed image labels.

Federated Learning Image Segmentation +4

Deep Understanding based Multi-Document Machine Reading Comprehension

no code implementations25 Feb 2022 Feiliang Ren, Yongkang Liu, Bochao Li, Zhibo Wang, Yu Guo, Shilei Liu, Huimin Wu, Jiaqi Wang, Chunchao Liu, Bingchao Wang

Most existing multi-document machine reading comprehension models mainly focus on understanding the interactions between the input question and documents, but ignore following two kinds of understandings.

Machine Reading Comprehension TriviaQA

Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation

1 code implementation22 Nov 2021 Huimin Wu, Xiaomeng Li, Kwang-Ting Cheng

A stage-adaptive contrastive learning method is proposed, containing a boundary-aware contrastive loss that takes advantage of the labeled images in the first stage, as well as a prototype-aware contrastive loss to optimize both labeled and pseudo labeled images in the second stage.

Contrastive Learning Image Segmentation +4

Efficient Semi-Supervised Adversarial Training without Guessing Labels

no code implementations29 Sep 2021 Huimin Wu, Heng Huang, Bin Gu

To adapt to semi-supervised learning problems, they need to estimate labels for unlabeled data in advance, which inevitably degenerates the performance of the learned model due to the bias on the estimation of labels for unlabeled data.

Fast and Scalable Adversarial Training of Kernel SVM via Doubly Stochastic Gradients

1 code implementation21 Jul 2021 Huimin Wu, Zhengmian Hu, Bin Gu

Although a wide range of researches have been done in recent years to improve the adversarial robustness of learning models, but most of them are limited to deep neural networks (DNNs) and the work for kernel SVM is still vacant.

Adversarial Robustness

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