CVPR 2023 – Boundary Unlearning

In this episode we discuss Boundary Unlearning
by Min Chen, Weizhuo Gao, Gaoyang Liu, Kai Peng, Chen Wang. The paper proposes “Boundary Unlearning” as an efficient machine unlearning technique to enable deep neural networks (DNNs) to unlearn, or forget, a fraction of training data and its lineage. The proposed method focuses on the decision space of the model rather than the parameter space, and involves shifting the decision boundary of the original DNN model to imitate the decision behavior of the model retrained from scratch. The proposed technique is evaluated on image classification and face recognition tasks, with expected speed-up compared to retraining from scratch.