Deep machine unlearning is the problem of removing the influence of a cohort of data from the weights of a trained deep model. This challenge is enjoying increasing attention due to the widespread use of neural networks in applications involving user data: allowing users to exercise their `right to be forgotten' necessitates an effective unlearning algorithm. However, deleting data from models is also of interest in practice for other applications where individual user privacy is not necessarily a consideration: removing biases, out-of-date examples, outliers, or noisy labels, and different such applications come with different desiderata. We propose a new unlearning algorithm (coined SCRUB) and conduct a comprehensive experimental evaluation against several previous state-of-the-art models. The results reveal that SCRUB is consistently a top performer across three different metrics for measuring unlearning quality, reflecting different application scenarios, while not degrading the model's performance.

翻译：深度机器遗忘问题是指从已训练深度模型的权重中消除某一数据群体影响的任务。由于神经网络在涉及用户数据的应用中广泛使用，这一挑战正受到日益增长的关注：允许用户行使“被遗忘权”需要一种有效的遗忘算法。然而，在实际应用中，删除模型中的数据同样具有重要意义，即使不必考虑个体用户隐私——例如消除偏差、过时样本、异常值或噪声标签——而不同的应用场景也对应着不同的需求。我们提出了一种新的遗忘算法（命名为SCRUB），并针对多个此前最先进模型开展了全面的实验评估。结果表明，SCRUB在衡量遗忘质量的三种不同指标上始终表现优异，这些指标反映了不同的应用场景，同时不会降低模型的性能。