Memory-Efficient Continual Learning with CLIP Models
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911649107017728 |
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| author | King, Ryan Li, Gang Mortazavi, Bobak Yang, Tianbao |
| author_facet | King, Ryan Li, Gang Mortazavi, Bobak Yang, Tianbao |
| contents | Contrastive Language-Image Pretraining (CLIP) models excel at understanding image-text relationships but struggle with adapting to new data without forgetting prior knowledge. To address this, models are typically fine-tuned using both new task data and a memory buffer of past tasks. However, CLIP's contrastive loss suffers when the memory buffer is small, leading to performance degradation on previous tasks. We propose a memory-efficient, distributionally robust method that dynamically reweights losses per class during training. Our approach, tested on class incremental settings (CIFAR-100, ImageNet1K) and a domain incremental setting (DomainNet) adapts CLIP models quickly while minimizing catastrophic forgetting, even with minimal memory usage. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_03866 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Memory-Efficient Continual Learning with CLIP Models King, Ryan Li, Gang Mortazavi, Bobak Yang, Tianbao Machine Learning Contrastive Language-Image Pretraining (CLIP) models excel at understanding image-text relationships but struggle with adapting to new data without forgetting prior knowledge. To address this, models are typically fine-tuned using both new task data and a memory buffer of past tasks. However, CLIP's contrastive loss suffers when the memory buffer is small, leading to performance degradation on previous tasks. We propose a memory-efficient, distributionally robust method that dynamically reweights losses per class during training. Our approach, tested on class incremental settings (CIFAR-100, ImageNet1K) and a domain incremental setting (DomainNet) adapts CLIP models quickly while minimizing catastrophic forgetting, even with minimal memory usage. |
| title | Memory-Efficient Continual Learning with CLIP Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.03866 |