May 14, 2024, 4:42 a.m. | Zixin Wang, Kongyang Chen

cs.LG updates on

arXiv:2405.07317v1 Announce Type: new
Abstract: Machine unlearning is a complex process that necessitates the model to diminish the influence of the training data while keeping the loss of accuracy to a minimum. Despite the numerous studies on machine unlearning in recent years, the majority of them have primarily focused on supervised learning models, leaving research on contrastive learning models relatively underexplored. With the conviction that self-supervised learning harbors a promising potential, surpassing or rivaling that of supervised learning, we set …

abstract accuracy arxiv cs.lg data influence loss machine minimum process research studies supervised learning them training training data type unlearning while

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