Multi-Class Unlearning for Image Classification via Weight Filtering
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
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| _version_ | 1866929377515667456 |
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| author | Poppi, Samuele Sarto, Sara Cornia, Marcella Baraldi, Lorenzo Cucchiara, Rita |
| author_facet | Poppi, Samuele Sarto, Sara Cornia, Marcella Baraldi, Lorenzo Cucchiara, Rita |
| contents | Machine Unlearning is an emerging paradigm for selectively removing the impact of training datapoints from a network. Unlike existing methods that target a limited subset or a single class, our framework unlearns all classes in a single round. We achieve this by modulating the network's components using memory matrices, enabling the network to demonstrate selective unlearning behavior for any class after training. By discovering weights that are specific to each class, our approach also recovers a representation of the classes which is explainable by design. We test the proposed framework on small- and medium-scale image classification datasets, with both convolution- and Transformer-based backbones, showcasing the potential for explainable solutions through unlearning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_02049 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Multi-Class Unlearning for Image Classification via Weight Filtering Poppi, Samuele Sarto, Sara Cornia, Marcella Baraldi, Lorenzo Cucchiara, Rita Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Machine Unlearning is an emerging paradigm for selectively removing the impact of training datapoints from a network. Unlike existing methods that target a limited subset or a single class, our framework unlearns all classes in a single round. We achieve this by modulating the network's components using memory matrices, enabling the network to demonstrate selective unlearning behavior for any class after training. By discovering weights that are specific to each class, our approach also recovers a representation of the classes which is explainable by design. We test the proposed framework on small- and medium-scale image classification datasets, with both convolution- and Transformer-based backbones, showcasing the potential for explainable solutions through unlearning. |
| title | Multi-Class Unlearning for Image Classification via Weight Filtering |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2304.02049 |