We address these flaws through a study of alternative self-supervised feature extractors, find that the semantic information encoded by individual networks strongly depends on their training procedure, and show that DINOv2-ViT -L/14 allows for much richer evaluation of generative models.

Deep machine unlearning is the problem of'removing' from a trained neural network a subset of its training set.

Machine learning models are often personalized by usinggroup attributesthat encodepersonalcharacteristics(e.g.,sex,agegroup,HIVstatus). Insuchsettings, individuals expect to receive more accurate predictions in return for disclosing group attributes to the personalized model.