糟糕的教学能诱发遗忘吗？利用不称职教师实现深度网络中的反学习
Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teacher
AI总结：
本文提出利用学生-教师框架中称职与不称职教师的选择性知识迁移实现深度网络反学习，并引入零重训遗忘（ZRF）指标，在多种深度网络和领域中验证了随机子集遗忘与类别遗忘的有效性和高效性。
摘要：
由于机器学习（ML）应用日益需要遵守新兴的数据隐私法规，机器反学习已成为一个重要的研究领域。它使得能够从已经训练好的ML模型中移除某一组或某一类数据，而无需从头重新训练。近年来，人们为提升反学习的有效性和效率做出了诸多努力。我们提出了一种新颖的机器反学习方法，通过在学生-教师框架中利用称职教师与不称职教师的效用，来诱导遗忘。来自称职教师和不称职教师的知识被选择性地迁移给学生，从而得到一个不包含任何关于遗忘数据信息的模型。我们通过实验表明，该方法泛化良好、速度快且有效。此外，我们引入了零重训遗忘（ZRF）指标来评估任何反学习方法。与现有的反学习指标不同，ZRF分数不依赖于昂贵的重训模型的可用性。这使得它在模型部署后也能用于分析反学习后的模型。我们展示了在多种深度网络上以及跨不同应用领域进行的随机子集遗忘和类别遗忘实验的结果。源代码位于：https://github.com/vikram2000b/bad-teaching-unlearning
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
