Knowledge Swapping via Learning and Unlearning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916616860598272 |
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| author | Xing, Mingyu Cheng, Lechao Tang, Shengeng Wang, Yaxiong Zhong, Zhun Wang, Meng |
| author_facet | Xing, Mingyu Cheng, Lechao Tang, Shengeng Wang, Yaxiong Zhong, Zhun Wang, Meng |
| contents | We introduce \textbf{Knowledge Swapping}, a novel task designed to selectively regulate knowledge of a pretrained model by enabling the forgetting of user\-specified information, retaining essential knowledge, and acquiring new knowledge simultaneously. By delving into the analysis of knock-on feature hierarchy, we find that incremental learning typically progresses from low\-level representations to higher\-level semantics, whereas forgetting tends to occur in the opposite direction\-starting from high-level semantics and moving down to low-level features. Building upon this, we propose to benchmark the knowledge swapping task with the strategy of \textit{Learning Before Forgetting}. Comprehensive experiments on various tasks like image classification, object detection, and semantic segmentation validate the effectiveness of the proposed strategy. The source code is available at \href{https://github.com/xingmingyu123456/KnowledgeSwapping}{https://github.com/xingmingyu123456/KnowledgeSwapping}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08075 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Knowledge Swapping via Learning and Unlearning Xing, Mingyu Cheng, Lechao Tang, Shengeng Wang, Yaxiong Zhong, Zhun Wang, Meng Computer Vision and Pattern Recognition We introduce \textbf{Knowledge Swapping}, a novel task designed to selectively regulate knowledge of a pretrained model by enabling the forgetting of user\-specified information, retaining essential knowledge, and acquiring new knowledge simultaneously. By delving into the analysis of knock-on feature hierarchy, we find that incremental learning typically progresses from low\-level representations to higher\-level semantics, whereas forgetting tends to occur in the opposite direction\-starting from high-level semantics and moving down to low-level features. Building upon this, we propose to benchmark the knowledge swapping task with the strategy of \textit{Learning Before Forgetting}. Comprehensive experiments on various tasks like image classification, object detection, and semantic segmentation validate the effectiveness of the proposed strategy. The source code is available at \href{https://github.com/xingmingyu123456/KnowledgeSwapping}{https://github.com/xingmingyu123456/KnowledgeSwapping}. |
| title | Knowledge Swapping via Learning and Unlearning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.08075 |