Memory-Efficient Continual Learning with CLIP Models
Continual learning remains a critical bottleneck for vision-language models in production. This work tackles catastrophic forgetting in CLIP by introducing a loss reweighting strategy that maintains performance on old tasks while learning new ones, even under severe memory constraints. The approach is validated across multiple incremental learning regimes (class and domain shifts), addressing a practical pain point for practitioners deploying CLIP at scale. The contribution matters because it bridges the gap between sample efficiency and retention, two properties that typically trade off in adapter-based fine-tuning workflows.
Modelwire context
ExplainerThe paper's core novelty is a loss reweighting strategy, not a new architecture. What matters is that it achieves the trade-off under severe memory constraints where prior work typically required choosing between retention and new-task performance.
This work sits in a cluster of recent papers tackling memory bottlenecks across modalities. The 'Make Your LVLM KV Cache More Lightweight' paper from May 1st addressed inference-time memory for vision-language models; this tackles training-time memory for continual learning. Both recognize that resource constraints force practitioners to abandon ideal solutions. The MemCoE framework from the same week approached memory as a learnable optimization problem for LLMs; this paper treats it as a loss-weighting problem for vision-language models. The constraint is shared, but the solution space differs by modality and task structure.
If this approach maintains performance parity with full-memory baselines on ImageNet1K domain shifts using less than 10% of the memory budget that prior adapter methods required, the method moves from incremental to practically deployable. Watch whether follow-up work applies the same reweighting strategy to other multimodal architectures (e.g., LLaVA, Flamingo) within the next six months; if it doesn't generalize, the contribution is CLIP-specific rather than foundational.
Coverage we drew on
- Make Your LVLM KV Cache More Lightweight · arXiv cs.LG
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MentionsCLIP · CIFAR-100 · ImageNet1K · DomainNet
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