A Retention-Centric Framework for Continual Learning with Guaranteed Model Developmental Safety

TL;DR

This paper introduces a retention-centric framework for continual learning that guarantees model developmental safety by preserving important old capabilities while learning new tasks, addressing risks of catastrophic forgetting in cost-sensitive applications.

Contribution

It proposes a novel data-dependent constrained optimization approach to ensure retention of key capabilities during continual model development, with theoretical guarantees and practical finetuning of CLIP models.

Findings

Effective in autonomous driving and scene recognition tasks

Ensures retention of important capabilities during continual learning

Provides theoretical guarantees for model safety

Abstract

In real-world applications, learning-enabled systems often undergo iterative model development to address challenging or emerging tasks, which involve collecting new data, training a new model and validating the model. This continual model development process raises a significant issue that acquiring new or improving existing capabilities may inadvertently lose good capabilities of the old model, also known as catastrophic forgetting. While existing continual learning aims to mitigate catastrophic forgetting by trading off performance on previous tasks and new tasks to ensure good average performance, it often falls short in cost-sensitive applications, where failing to preserve essential established capabilities introduces unforeseen costs and risks and substantial expenses for re-improving these capabilities. To address this issue, we impose a requirement on learning systems to ensure that a new model strictly retains important capabilities of the old model while improving target-task performance, which we term model developmental safety. To ensure model developmental safety, we propose a retention-centric framework with data-dependent constraints, and study how to continually develop a pretrained CLIP model for acquiring new or improving existing capabilities of image classification. We propose an efficient constrained optimization algorithm with theoretical guarantees and use its insights to finetune the CLIP model with task-dependent heads for promoting the model developmental safety. Experiments on autonomous driving and scene recognition datasets validate the efficacy of our method.