A Comparative Study of Machine Unlearning Techniques for Image and Text Classification Models

• URL: http://arxiv.org/abs/2412.19583v1
• Date: Fri, 27 Dec 2024 10:58:55 GMT
• Title: A Comparative Study of Machine Unlearning Techniques for Image and Text Classification Models
• Authors: Omar M. Safa, Mahmoud M. Abdelaziz, Mustafa Eltawy, Mohamed Mamdouh, Moamen Gharib, Salaheldin Eltenihy, Nagia M. Ghanem, Mohamed M. Ismail,
• Abstract summary: Machine Unlearning has emerged as a critical area in artificial intelligence, addressing the need to selectively remove learned data from machine learning models. This paper provides a comprehensive comparative analysis of six state-of-theart unlearning techniques applied to image and text classification tasks.
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• 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.

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