Convolutional Prompting meets Language Models for Continual Learning

Understand

Continual Learning (CL) enables machine learning models to learn from continuously shifting new training data in absence of data from old tasks.

• Recently, pretrained vision transformers combined with prompt tuning have shown promise for overcoming catastrophic forgetting in CL.
• These approaches rely on a pool of learnable prompts which can be inefficient in sharing knowledge across tasks leading to inferior performance.
• In addition, the lack of fine-grained layer specific prompts does not allow these to fully express the strength of the prompts for CL.