Deep learning has achieved significant improvements in accuracy and has been applied to various fields. With the spread of deep learning, a new problem has also emerged; deep learning models can sometimes have undesirable information from an ethical standpoint. This problem must be resolved if deep learning is to make sensitive decisions such as hiring and prison sentencing. Machine unlearning (MU) is the research area that responds to such demands. MU aims at forgetting about undesirable training data from a trained deep learning model. A naive MU approach is to re-train the whole model with the training data from which the undesirable data has been removed. However, re-training the whole model can take a huge amount of time and consumes significant computer resources. To make MU even more practical, a simple-yet-effective MU method is required. In this paper, we propose a one-shot MU method, which does not need additional training. To design one-shot MU, we add noise to the model parameters that are sensitive to undesirable information. In our proposed method, we use the Fisher information matrix (FIM) to estimate the sensitive model parameters. Training data were usually used to evaluate the FIM in existing methods. In contrast, we avoid the need to retain the training data for calculating the FIM by using class-specific synthetic signals called mnemonic code. Extensive experiments using artificial and natural datasets demonstrate that our method outperforms the existing methods.

翻译：深度学习在准确性方面取得了显著提升，并已应用于多个领域。随着深度学习的普及，一个新问题也随之浮现：深度学习模型有时可能包含从伦理角度看不良的信息。若深度学习要用于做出如招聘和量刑等敏感决策，这一问题必须得到解决。机器遗忘（MU）正是应对此类需求的研究领域，其目标是从已训练的深度学习模型中遗忘不良训练数据。一种简单的机器遗忘方法是使用移除不良数据后的训练数据重新训练整个模型，但这会耗费大量时间并占用大量计算资源。为使机器遗忘更具实用性，迫切需要一种简单而有效的方法。本文提出了一种无需额外训练的单样本机器遗忘方法。为实现单样本遗忘，我们对对不良信息敏感的模型参数添加噪声。在所提出的方法中，我们使用Fisher信息矩阵（FIM）来估计敏感模型参数。现有方法通常使用训练数据来评估FIM，而我们则通过称为记忆码的类别特定合成信号来避免保留训练数据以计算FIM。利用人工和自然数据集进行的大量实验表明，我们的方法优于现有方法。