Replay-free Online Continual Learning with Self-Supervised MultiPatches
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916612127326208 |
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| author | Cignoni, Giacomo Cossu, Andrea Gomez-Villa, Alex van de Weijer, Joost Carta, Antonio |
| author_facet | Cignoni, Giacomo Cossu, Andrea Gomez-Villa, Alex van de Weijer, Joost Carta, Antonio |
| contents | Online Continual Learning (OCL) methods train a model on a non-stationary data stream where only a few examples are available at a time, often leveraging replay strategies. However, usage of replay is sometimes forbidden, especially in applications with strict privacy regulations. Therefore, we propose Continual MultiPatches (CMP), an effective plug-in for existing OCL self-supervised learning strategies that avoids the use of replay samples. CMP generates multiple patches from a single example and projects them into a shared feature space, where patches coming from the same example are pushed together without collapsing into a single point. CMP surpasses replay and other SSL-based strategies on OCL streams, challenging the role of replay as a go-to solution for self-supervised OCL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09140 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Replay-free Online Continual Learning with Self-Supervised MultiPatches Cignoni, Giacomo Cossu, Andrea Gomez-Villa, Alex van de Weijer, Joost Carta, Antonio Machine Learning Computer Vision and Pattern Recognition I.2.6 Online Continual Learning (OCL) methods train a model on a non-stationary data stream where only a few examples are available at a time, often leveraging replay strategies. However, usage of replay is sometimes forbidden, especially in applications with strict privacy regulations. Therefore, we propose Continual MultiPatches (CMP), an effective plug-in for existing OCL self-supervised learning strategies that avoids the use of replay samples. CMP generates multiple patches from a single example and projects them into a shared feature space, where patches coming from the same example are pushed together without collapsing into a single point. CMP surpasses replay and other SSL-based strategies on OCL streams, challenging the role of replay as a go-to solution for self-supervised OCL. |
| title | Replay-free Online Continual Learning with Self-Supervised MultiPatches |
| topic | Machine Learning Computer Vision and Pattern Recognition I.2.6 |
| url | https://arxiv.org/abs/2502.09140 |