Machine Unlearning: A Survey
Citations Over TimeTop 1% of 2023 papers
Abstract
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.
Related Papers
- → A Usability User Study Concerning Free-Hand Microgesture and Wrist-Worn Sensors(2014)31 cited
- → A Novel Algorithmic Approach for an Automatic Data Placement for NUMA Based Design(2016)1 cited
- → New Set of Metrics for Accessing Usability in Feature Oriented Programming(2013)2 cited
- → A method for assessing the usability of an on screen display for a brain-computer interface(2014)1 cited
- Research on the Key Algorithms of HNC Question Answering System(2006)