论文标题
不好的教导会忘记吗？使用无能的老师在深网中学习
Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teacher
论文作者
论文摘要
由于越来越多的机器学习应用程序（ML）应用程序符合新兴数据隐私法规，因此机器的学习已成为研究的重要领域。它有助于从已训练的ML模型中删除某些或类别的数据的规定，而无需从头开始重新审阅。最近，已经付出了一些努力，以使其无法实现有效和高效的效率。我们通过在学生教师框架中探索有能力和无能的老师的实用性来引起健忘，提出了一种新颖的机器学习方法。有能力和无能的教师的知识被选择性地转移到学生中，以获取不包含有关忘记数据的任何信息的模型。我们从实验上表明，该方法良好，快速有效。此外，我们引入了零重新培训遗忘（ZRF）度量，以评估任何未学习方法。与现有的未学习指标不同，ZRF分数不取决于昂贵的再培训模型的可用性。这也使得对部署后的未学习模型进行分析有用。我们介绍了为随机子集遗忘和类遗忘在各种深层网络和不同应用程序域上进行的实验结果。
Machine unlearning has become an important area of research due to an increasing need for machine learning (ML) applications to comply with the emerging data privacy regulations. It facilitates the provision for removal of certain set or class of data from an already trained ML model without requiring retraining from scratch. Recently, several efforts have been put in to make unlearning to be effective and efficient. We propose a novel machine unlearning method by exploring the utility of competent and incompetent teachers in a student-teacher framework to induce forgetfulness. The knowledge from the competent and incompetent teachers is selectively transferred to the student to obtain a model that doesn't contain any information about the forget data. We experimentally show that this method generalizes well, is fast and effective. Furthermore, we introduce the zero retrain forgetting (ZRF) metric to evaluate any unlearning method. Unlike the existing unlearning metrics, the ZRF score does not depend on the availability of the expensive retrained model. This makes it useful for analysis of the unlearned model after deployment as well. We present results of experiments conducted for random subset forgetting and class forgetting on various deep networks and across different application domains.~Source code is at: https://github.com/vikram2000b/bad-teaching-unlearning
