Learning from Itself: Mining Internal Knowledge from Vision Language Models for Continual Learning

Abstract

Vision-language models like CLIP excel at zero-shot recognition but struggle with continual learning due to two critical issues: (1) severe distribution gap between pretraining captions and post-training class names, and (2) performance mismatch between vision-only and dual-encoder approaches--vision-only methods achieve 20% higher accuracy on fine-grained tasks while CLIP dominates on natural images. We propose Learning from Itself (LfI), which mines CLIP's internal knowledge to address both challenges. First, we generate pseudo-captions by optimizing learnable tokens to minimize CLIP's contrastive loss, creating auxiliary training signals that bridge the pretraining-finetuning distribution gap without external models. Second, we introduce adaptive mutual distillation that dynamically weights knowledge transfer between CLIP's text encoder and a temporary vision classifier based on their instantaneous performance--stronger branches teach more, weaker ones learn more. At inference, only the original CLIP architecture is used, having absorbed discriminative knowledge from both branches. LfI achieves state-of-the-art results across multiple continual learning benchmarks, demonstrating that CLIP can effectively teach itself to continually learn new tasks. Code is available at: https://github.com/jordangong/continual-learning.