Lifelong Learning and Selective Forgetting via Contrastive Strategy
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917677758414848 |
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| author | Shan, Lianlei Zhou, Wenzhang Li, Wei Ding, Xingyu |
| author_facet | Shan, Lianlei Zhou, Wenzhang Li, Wei Ding, Xingyu |
| contents | Lifelong learning aims to train a model with good performance for new tasks while retaining the capacity of previous tasks. However, some practical scenarios require the system to forget undesirable knowledge due to privacy issues, which is called selective forgetting. The joint task of the two is dubbed Learning with Selective Forgetting (LSF). In this paper, we propose a new framework based on contrastive strategy for LSF. Specifically, for the preserved classes (tasks), we make features extracted from different samples within a same class compacted. And for the deleted classes, we make the features from different samples of a same class dispersed and irregular, i.e., the network does not have any regular response to samples from a specific deleted class as if the network has no training at all. Through maintaining or disturbing the feature distribution, the forgetting and memory of different classes can be or independent of each other. Experiments are conducted on four benchmark datasets, and our method acieves new state-of-the-art. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18663 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Lifelong Learning and Selective Forgetting via Contrastive Strategy Shan, Lianlei Zhou, Wenzhang Li, Wei Ding, Xingyu Artificial Intelligence Lifelong learning aims to train a model with good performance for new tasks while retaining the capacity of previous tasks. However, some practical scenarios require the system to forget undesirable knowledge due to privacy issues, which is called selective forgetting. The joint task of the two is dubbed Learning with Selective Forgetting (LSF). In this paper, we propose a new framework based on contrastive strategy for LSF. Specifically, for the preserved classes (tasks), we make features extracted from different samples within a same class compacted. And for the deleted classes, we make the features from different samples of a same class dispersed and irregular, i.e., the network does not have any regular response to samples from a specific deleted class as if the network has no training at all. Through maintaining or disturbing the feature distribution, the forgetting and memory of different classes can be or independent of each other. Experiments are conducted on four benchmark datasets, and our method acieves new state-of-the-art. |
| title | Lifelong Learning and Selective Forgetting via Contrastive Strategy |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2405.18663 |