Stream Learning (SL) requires models to rapidly adapt to continuous data
streams, setting it apart from traditional Continual Learning (CL). Recent SL
methods emphasize efficiency by selecting data subsets for training, but they
often struggle due to their reliance on static, rule-based selection algorithms
that cannot effectively adapt to the changing importance of data. In this work,
we introduce StreamPrompt, a method that enhances data selection through
dynamic, learnable prompts. These dynamic prompts serve two purposes beyond
guiding model inference: 1) optimizing data selection, and 2) guiding updates
to the rehearsal buffer. This approach addresses the challenges of adaptability
and computational efficiency in processing continuous data streams. Moreover,
StreamPrompt introduces Prompt Attunement,a mechanism that enhances the
efficiency of prompt learning. By leveraging attention layers from vision
transformers and softly combining their outputs with a gate unit, Prompt
Attunementrefines prompts with minimal computational resources. Comprehensive
evaluations demonstrate StreamPrompts superior performance over
state-of-the-art, with significant improvements in accuracy and reductions in
training time. These results underscore the efficacy and efficiency of
StreamPrompt, establishing its potential as a scalable and effective solution
for the evolving demands of SL. Our code is available at
this https URL

StreamPrompt 使用动态的、可学习的提示方法增强数据选择，以解决流学习中的适应性和计算效率问题，并通过 Prompt Attunement 机制提高了提示的效率。在全面评估中，StreamPrompt 表现优越，并在准确性和训练时间上实现了显著的提升，从而确立了其作为可扩展和有效解决连续数据流不断变化需求的解决方案的潜力。