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.

翻译：虽然传统机器学习能有效解决广泛问题，但其主要在封闭世界设定下运行，在处理流式数据时存在局限性。作为解决方案，增量学习应运而生以应对涉及新数据到达的真实场景。近年来，预训练技术取得显著进展并吸引了众多研究者的关注。这些预训练模型（PTMs）的强大性能为开发能有效适应真实场景的持续学习算法提供了可行方向。因此，探索PTMs在增量学习中的应用已成为研究重点。本文介绍了一个名为PILOT的基于预训练模型的持续学习工具箱。一方面，PILOT实现了若干基于预训练模型的最先进类增量学习算法，包括L2P、DualPrompt和CODA-Prompt。另一方面，PILOT还将典型类增量学习算法（如DER、FOSTER和MEMO）适配到预训练模型框架下以评估其有效性。