ViT-MUL: A Baseline Study on Recent Machine Unlearning Methods Applied
  to Vision Transformers
Ikhyun Cho Changyeon Park Julia Hockenmaier
Machine unlearning (MUL) is an arising field in machine learning that seeks
to erase the learned information of specific training data points from a
trained model. Despite the recent active research in MUL within computer
vision, the majority of work has focused on ResNet-based models. Given that
Vision Transformers (ViT) have become the predominant model architecture, a
detailed study of MUL specifically tailored to ViT is essential. In this paper,
we present comprehensive experiments on ViTs using recent MUL algorithms and
datasets. We anticipate that our experiments, ablation studies, and findings
could provide valuable insights and inspire further research in this field.