ERM-KTP: Knowledge-Level Machine Unlearning via Knowledge Transfer

Publication Name

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition


Machine unlearning can fortify the privacy and security of machine learning applications. Unfortunately, the exact unlearning approaches are inefficient, and the approximate unlearning approaches are unsuitable for complicated CNNs. Moreover, the approximate approaches have serious security flaws because even unlearning completely different data points can produce the same contribution estimation as unlearning the target data points. To address the above problems, we try to define machine unlearning from the knowledge perspective, and we propose a knowledge-level machine unlearning method, namely ERM-KTP. Specifically, we propose an entanglement-reduced mask (ERM) structure to reduce the knowledge entanglement among classes during the training phase. When receiving the un-learning requests, we transfer the knowledge of the non-target data points from the original model to the unlearned model and meanwhile prohibit the knowledge of the target data points via our proposed knowledge transfer and prohibition (KTP) method. Finally, we will get the un-learned model as the result and delete the original model to accomplish the unlearning process. Especially, our proposed ERM-KTP is an interpretable unlearning method because the ERM structure and the crafted masks in KTP can explicitly explain the operation and the effect of un-learning data points. Extensive experiments demonstrate the effectiveness, efficiency, high fidelity, and scalability of the ERM-KTP unlearning method. Code is available at

Open Access Status

This publication is not available as open access



First Page


Last Page


Funding Number


Funding Sponsor

National Natural Science Foundation of China



Link to publisher version (DOI)