no code implementations • 26 Apr 2024 • Yuhang Huang, Zihan Wu, Chongyang Gao, Jiawei Peng, Xu Yang
Large Vision-Language Models (LVLMs) are gaining traction for their remarkable ability to process and integrate visual and textual data.
1 code implementation • 17 Apr 2024 • Jie Xu, Zihan Wu, Cong Wang, Xiaohua Jia
To address the growing demand for privacy protection in machine learning, we propose a novel and efficient machine unlearning approach for \textbf{L}arge \textbf{M}odels, called \textbf{LM}Eraser.
no code implementations • 14 Dec 2023 • Wentao Pan, Zhe Xu, Jiangpeng Yan, Zihan Wu, Raymond Kai-yu Tong, Xiu Li, Jianhua Yao
Semi-supervised semantic segmentation aims to utilize limited labeled images and abundant unlabeled images to achieve label-efficient learning, wherein the weak-to-strong consistency regularization framework, popularized by FixMatch, is widely used as a benchmark scheme.
no code implementations • 1 Dec 2023 • Haokun Chen, Xu Yang, Yuhang Huang, Zihan Wu, Jing Wang, Xin Geng
Specifically, using our approach on ImageNet, we increase accuracy from 74. 70\% in a 4-shot setting to 76. 21\% with just 2 shots.
1 code implementation • 14 Aug 2023 • Jie Xu, Zihan Wu, Cong Wang, Xiaohua Jia
Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious data, posing risks of privacy breaches, security vulnerabilities, and performance degradation.
no code implementations • 9 Jun 2023 • Zihan Wu, Neil Scheidwasser-Clow, Karl El Hajal, Milos Cernak
However, the benchmark only evaluates performance separately on each dataset, but does not evaluate performance across the different types of stress and different languages.
no code implementations • 12 Nov 2022 • Karl El Hajal, Zihan Wu, Neil Scheidwasser-Clow, Gasser Elbanna, Milos Cernak
Automatic speech quality assessment is essential for audio researchers, developers, speech and language pathologists, and system quality engineers.
no code implementations • journal 2022 • Zihan Wu, HONGZHANG, PENGHAI WANG, ANDZHIBOSUN
In this paper, we propose a Robust Transformer-based Intrusion Detection System(RTIDS)reconstructingfeaturerepresentationstomakeatrade-offbetweendimensionalityreduction and feature retention in imbalanced datasets.