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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/157012

Title:	Investigation of Feature Distribution and Network Weight Updates in the Machine Unlearning Process
Authors:	廖文宏 Liao, Wen-Hung;Lin, Yang-Jing
Contributors:	資訊系
Keywords:	Machine Unlearning;Label Reassignment;Model Manipulation;Weight Pruning
Date:	2024-12
Issue Date:	2025-05-19 11:44:30 (UTC+8)
Abstract:	Machine unlearning refers to the process of expunging previously learned information and data from a machine learning model to achieve the objective of privacy protection. In this research, we explore two prevalent methods for unlearning, namely, label reassignment and model manipulation. Using the CIFAR-100 classification problem with ResNet-50 architecture as an example, we examine the efficacy of these two mechanisms and their variants in the corresponding unlearning task. We further investigate the changes in feature distribution and the extent of weight updates across various network layers throughout the unlearning process. Experimental results indicate that the degree of network variation is proportional to the number of removed classes. When employing the label reassignment method, the variation is concentrated in the final stage and fully connected layers. On the other hand, using the weight resetting strategy affects more network layers, with the impact gradually decreasing from the later layers to the middle and earlier layers. Overall, when the categories to be forgotten are less than 10%, no significant impact on feature extraction is observed.
Relation:	2024 International Symposium on Multimedia (ISM), IEEE Technical Committee on Multimedia (TCMC)
Data Type:	conference
DOI 連結:	https://doi.org/10.1109/ISM63611.2024.00022
DOI:	10.1109/ISM63611.2024.00022
Appears in Collections:	[資訊科學系] 會議論文

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