PILOT：一个基于预训练模型的持续学习工具箱
PILOT: A Pre-Trained Model-Based Continual Learning Toolbox
AI总结：
本文提出基于预训练模型的持续学习工具箱PILOT，既实现L2P、DualPrompt等先进类增量算法，又将DER、FOSTER等传统算法适配到预训练模型框架中以评估其效果。
摘要：
虽然传统机器学习能够有效解决广泛的问题，但它主要在封闭世界设定下运行，这在处理流式数据时存在局限性。作为一种解决方案，增量学习应运而生，以应对涉及新数据到达的现实场景。近年来，预训练取得了重大进展，并引起了众多研究者的关注。这些预训练模型（PTMs）的强劲性能为开发能够有效适应现实场景的持续学习算法提供了一条有前景的途径。因此，探索在增量学习中利用预训练模型已变得至关重要。本文介绍了一个基于预训练模型的持续学习工具箱，称为PILOT。一方面，PILOT实现了一些基于预训练模型的最先进类增量学习算法，如L2P、DualPrompt和CODA-Prompt。另一方面，PILOT还将典型的类增量学习算法（例如DER、FOSTER和MEMO）适配到预训练模型的背景下，以评估它们的有效性。
英文摘要：
While traditional machine learning can effectively tackle a wide range of problems, it primarily operates within a closed-world setting, which presents limitations when dealing with streaming data. As a solution, incremental learning emerges to address real-world scenarios involving new data's arrival. Recently, pre-training has made significant advancements and garnered the attention of numerous researchers. The strong performance of these pre-trained models (PTMs) presents a promising avenue for developing continual learning algorithms that can effectively adapt to real-world scenarios. Consequently, exploring the utilization of PTMs in incremental learning has become essential. This paper introduces a pre-trained model-based continual learning toolbox known as PILOT. On the one hand, PILOT implements some state-of-the-art class-incremental learning algorithms based on pre-trained models, such as L2P, DualPrompt, and CODA-Prompt. On the other hand, PILOT also fits typical class-incremental learning algorithms (e.g., DER, FOSTER, and MEMO) within the context of pre-trained models to evaluate their effectiveness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
