Jingwen Ye

Welcome to my homepage! I'm Jingwen Ye, currently a research fellowship at Learning and Vision Lab, National University of Singapore， working with Prof. Xinchao Wang. I received my PhD from Zhejiang University, studying computer vision under Prof. Mingli Song and Chun Chen. Prior to that, I got my B. Eng. Degree from Dalian University of Technology.

My current research interests are mainly about Privacy-aware Transfer Learning and Effective Model Reusing. Specially, I focus on the privacy issues on the AIGC models. Also I investigate deeper with knowledge distillation and amalgamation techniques to improve the performance of the multi-task networks.

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We introduce an efficient framework for assessing data impact, comprising offline training and online evaluation stages.

UGEs exhibit learnability for authorized users while maintaining unlearnability for potential hackers.

We propose a novel approach that generates adversarial attacks in a mutual-modality optimization scheme.

PNC conducts partial parametric "cloning" from a source network and then injects the cloned module to the target, without modifying its parameters.

LIRF explicitly handles the task- or sample-specific knowledge removal and recovery.

SDB allows us to wrap a pre-trained model in a virtual box for intellectual property protection.