LW2G: Learning Whether to Grow for Prompt-based Continual Learning

qian feng, Da-Wei Zhou, Hanbin Zhao, Chao Zhang, Hui Qian

Keywords: Continual Learning, Gradient Orthogonal, Pre-Trained Model, Prompt Learning

TL;DR: LW2G employs a novel approach (HFC) to dynamically determine whether to add a new set of prompts or reuse existing ones based on the relationships between tasks.

Abstract: Continual Learning (CL) aims to learn in non-stationary scenarios, progressively acquiring and maintaining knowledge from sequential tasks. Recent Prompt-based Continual Learning (PCL) has achieved remarkable performance with Pre-Trained Models (PTMs). These approaches grow a prompt sets pool by adding a new set of prompts when learning each new task (prompt learning) and adopt a matching mechanism to select the correct set for each testing sample (prompt retrieval). Previous studies focus on the latter stage by improving the matching mechanism to enhance Prompt Retrieval Accuracy (PRA). To promote cross-task knowledge facilitation and form an effective and efficient prompt sets pool, we propose a plug-in module in the former stage to Learn Whether to Grow (LW2G) based on the disparities between tasks. Specifically, a shared set of prompts is utilized when several tasks share certain commonalities, and a new set is added when there are significant differences between the new task and previous tasks. Inspired by Gradient Projection Continual Learning, our LW2G develops a metric called Hinder Forward Capability (HFC) to measure the hindrance imposed on learning new tasks by surgically modifying the original gradient onto the orthogonal complement of the old feature space. With HFC, an automated scheme Dynamic Growing Approach adaptively learns whether to grow with a dynamic threshold. Furthermore, we design a gradient-based constraint to ensure the consistency between the updating prompts and pre-trained knowledge, and a prompts weights reusing strategy to enhance forward transfer. Extensive experiments show the effectiveness of our method.

Primary Area: transfer learning, meta learning, and lifelong learning

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Submission Number: 5800