Learning to Prompt for Continual Learning

🤖 Plain-English Summary

The mainstream paradigm behind continual learning has been to adapt the model parameters to non-stationary data distributions, where catastrophic forgetting is the central challenge. Surprisingly, L2P achieves competitive results against rehearsal-based methods even without a re-hearsal buffer and is directly applicable to challenging task-agnostic continual learning.

🔑 Key Findings

• Typical methods rely on a rehearsal buffer or known task identity at test time to retrieve learned knowl-edge and address forgetting, while this work presents a new paradigm for continual learning that aims to train a more succinct memory system without accessing task identity at test time.
• Our method learns to dynamically prompt (L2P) a pre-trained model to learn tasks sequen-tially under different task transitions.
• In our proposed framework, prompts are small learnable parameters, which are maintained in a memory space.

🔗 Related Resources