A Comparative Study of Machine Unlearning Techniques for Image and Text Classification Models
Year: 2024
Type: preprint
Abstract: Machine Unlearning has emerged as a critical area in artificial intelligence, addressing the need to selectively remove learned data from machine learning models in response to data privacy regulations. This paper provides a comprehensive comparative analysis of six state-of-theart unlearning techniques applied to image and text classification tasks. We evaluate their performance, efficiency, and compliance with regulatory requirements, highlighting their strengths and limitations in practical scenarios. By systematically analyzing these methods, we aim to provide insights into their applicability, challenges,and tradeoffs, fostering advancements in the field of ethical and adaptable machine learning.
Source: arXiv (Cornell University)
Authors: Safa, Omar M., Abdelaziz, Mahmoud M., Eltawy, Mustafa, Mamdouh, Mohamed, Gharib, Moamen, Eltenihy, Salaheldin, Ghanem, Nagia M., Ismail, Mohamed M.
Institution: Alexandria University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4405903359