3 papers
cs.CV2024
MUNBa: Machine Unlearning via Nash Bargaining
Jing Wu, Mehrtash Harandi
Machine Unlearning (MU) aims to selectively erase harmful behaviors from models while retaining the overall utility of the model. As a multi-task learning problem, MU involves bala…
cs.LG2024
Scissorhands: Scrub Data Influence via Connection Sensitivity in Networks
Jing Wu, Mehrtash Harandi
Machine unlearning has become a pivotal task to erase the influence of data from a trained model. It adheres to recent data regulation standards and enhances the privacy and securi…
cs.CV2024
Erasing Undesirable Influence in Diffusion Models
Jing Wu, Trung Le, Munawar Hayat +1
Diffusion models are highly effective at generating high-quality images but pose risks, such as the unintentional generation of NSFW (not safe for work) content. Although various t…