Memory-Efficient Continual Learning with CLIP Models

• URL: http://arxiv.org/abs/2605.03866v1
• Date: Tue, 05 May 2026 15:27:27 GMT
• Title: Memory-Efficient Continual Learning with CLIP Models
• Abstract summary: Contrastive Language-Image Pretraining (CLIP) models excel at understanding image-text relationships but struggle with adapting to new data without forgetting prior knowledge.<n>We propose a memory-efficient, distributionally robust method that dynamically reweights losses per class during training.
• Score: 33.36937397179743
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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