Regularization-Based Machine Unlearning

Abstract

This work treats the unlearning problem in machine learning (ML). This is the process to make ML models forget some subset of their training data. We restrict this study to deep learning architectures. We propose a metric to assess different unlearning algorithms. We design a new unlearning algorithm, Regret, and compare its performance with respect to Fine-tuning and our implementation of Fanchuan. We test them on four datasets and two different architectures. The experiments reveal that Regret outperforms Fine-tuning by a small margin. Moreover, our implementation of Fanchuan is the best-performing algorithm and surpasses the other two clearly.