论文标题
终身监督学习简介
An Introduction to Lifelong Supervised Learning
论文作者
论文摘要
该底漆是为了提供终身学习不同方面的详细摘要。我们从第2章开始，该第2章提供了终身学习系统的高级概述。 In this chapter, we discuss prominent scenarios in lifelong learning (Section 2.4), provide 8 Introduction a high-level organization of different lifelong learning approaches (Section 2.5), enumerate the desiderata for an ideal lifelong learning system (Section 2.6), discuss how lifelong learning is related to other learning paradigms (Section 2.7), describe common metrics used to evaluate lifelong learning systems (Section 2.8).对于那些毕生学习并希望在不关注特定方法或基准测试的情况下介绍到现场的读者，本章更有用。其余的章节专注于特定方面（学习算法或基准测试），对于正在寻找特定方法或基准的读者而言，更有用。第3章重点介绍基于正则化的方法，这些方法不假设从先前任务中访问任何数据。第4章讨论了基于内存的方法，这些方法通常使用重播缓冲区或情节内存来节省不同任务的数据子集。第5章侧重于为培训终身学习系统提出的不同建筑系列（及其实例化）。遵循这些不同类别的学习算法，我们讨论了终身学习的常用评估基准和指标（第6章），并讨论了第7章中未来的挑战和重要研究方向的讨论。
This primer is an attempt to provide a detailed summary of the different facets of lifelong learning. We start with Chapter 2 which provides a high-level overview of lifelong learning systems. In this chapter, we discuss prominent scenarios in lifelong learning (Section 2.4), provide 8 Introduction a high-level organization of different lifelong learning approaches (Section 2.5), enumerate the desiderata for an ideal lifelong learning system (Section 2.6), discuss how lifelong learning is related to other learning paradigms (Section 2.7), describe common metrics used to evaluate lifelong learning systems (Section 2.8). This chapter is more useful for readers who are new to lifelong learning and want to get introduced to the field without focusing on specific approaches or benchmarks. The remaining chapters focus on specific aspects (either learning algorithms or benchmarks) and are more useful for readers who are looking for specific approaches or benchmarks. Chapter 3 focuses on regularization-based approaches that do not assume access to any data from previous tasks. Chapter 4 discusses memory-based approaches that typically use a replay buffer or an episodic memory to save subset of data across different tasks. Chapter 5 focuses on different architecture families (and their instantiations) that have been proposed for training lifelong learning systems. Following these different classes of learning algorithms, we discuss the commonly used evaluation benchmarks and metrics for lifelong learning (Chapter 6) and wrap up with a discussion of future challenges and important research directions in Chapter 7.
