Learning Probabilistic Prompt for Continual Learning

Continual learning aims to progressively learn from a sequenceof tasks, each containing a disjoint subset of classes, while preservingpreviously learned knowledge. Prompt-based continual learning methodspropose to learn a small set of parameters, i.e., prompts, by associatingthem with a query feature of an input image. These methods optimize theprompts, attempting to represent diverse patterns of images. However,we have observed that existing prompt-based methods suffer from aprompt collapse problem, that is, the prompts tend to be highly similarto each other, thereby failing to capture the diverse data distributionsin continual learning scenarios. To address this issue, we propose in thispaper a novel prompt-based continual learning framework that capturesdiverse patterns of images across a sequence of tasks. To this end, wemodel each prompt as a probabilistic distribution and construct a mixtureof these distributions, from which we sample diverse prompts. This enablesour model to effectively capture highly diverse image distributions in thecontinual learning process. We also present a distribution regularizationloss to prevent abrupt changes in the prompt distributions throughoutthe training process. We show extensive experimental results for continuallearning on standard benchmarks, including ImageNet-R, CIFAR-100,and CUB-200, demonstrating the effectiveness of our framework.