Machine learning has attracted widespread attention and evolved into an enabling technology for a wide range of highly successful applications, such as intelligent computer vision, speech recognition, medical diagnosis, and more. Yet a special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning. This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality. At the same time, this ambitious problem has led to numerous research efforts aimed at confronting its challenges. To the best of our knowledge, no study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios. Accordingly, with this survey, we aim to capture the key concepts of unlearning techniques. The existing solutions are classified and summarized based on their characteristics within an up-to-date and comprehensive review of each category's advantages and limitations. The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities.

翻译：机器学习已获得广泛关注，并发展成为支撑众多成功应用的关键技术，如智能计算机视觉、语音识别、医学诊断等。然而，由于隐私保护、可用性及“被遗忘权”等需求，需要从模型中移除特定样本信息，这一技术被称为“机器反学习”。这一新兴技术因其创新性和实用性吸引了学术界与工业界的广泛关注。同时，这一具有挑战性的问题催生了大量旨在攻克其难关的研究工作。据我们所知，目前尚无研究系统分析这一复杂课题，或比较现有反学习方法在不同场景下的可行性。因此，本综述旨在梳理反学习技术的核心概念。我们基于现有解决方案的特征对其进行分类和总结，对每一类方法的优势与局限性进行最新且全面的评述。最后，本综述指出了反学习技术中尚未解决的突出问题，并提出了若干可行的新研究方向。