From: NeurIPS.cc/2021/Workshop/DistShift/Program_Chairs
title: Paper Decision
decision: Accept (Poster)
comment: The authors propose to obtain a model with good domain generalization performance by knowledge distillation from an ensemble of source-data trained models (without access to the data during distillation - also a new setting that they propose). Compared to other more straight-forward data-free models their approach achieves good performance. There is still a gap with data-intensive methods but this is clearly a relevant setting especially wrt privacy concerns etc. The reviewers like the clarity of the exposition and experimental rigor. A discussion on the relation to federated DG/DA would be interesting.