摘要:
近年来，随着信息技术的不断发展，各种数据呈现爆炸式的增长，传统的机器学习算法只有当测试数据与训练数据分布类似时，学习算法才能取得较好的性能，换句话说，它们不能在动态环境中连续自适应地学习，然而，这种自适应学习的能力却是任何智能系统都具备的特性.深度神经网络在许多应用中显示出最好的学习能力，然而，使用该方法对数据进行增量更新学习时，会面临灾难性的干扰或遗忘问题，导致模型在学习新任务之后忘记如何解决旧任务.连续学习(continual learning, CL)的研究使这一问题得到缓解.连续学习是模拟大脑学习的过程，按照一定的顺序对连续非独立同分布的(independently and identically distributed, IID)流数据进行学习，进而根据任务的执行结果对模型进行增量式更新.连续学习的意义在于高效地转化和利用已经学过的知识来完成新任务的学习，并且能够极大程度地降低遗忘带来的问题.连续学习研究对智能计算系统自适应地适应环境改变具有重要的意义.基于此，系统综述了连续学习的研究进展，首先概述了连续学习的定义，介绍了无遗忘学习、弹性权重整合和梯度情景记忆3种典型的连续学习模型，并对连续学习存在的关键问题及解决方法进行了介绍，之后又对基于正则化、动态结构和记忆回放互补学习系统的3类连续学习模型进行了分类和阐述，并在最后指明了连续学习进一步研究中需要解决的问题以及未来可能的发展方向.
Abstract:
In recent years, with the continuous development of information technology, all kinds of data have shown explosive growth. Traditional machine learning algorithms can only achieve better performance when the distribution of testing data and training data is similar. In other words, it is impossible to continuously and adaptively learn in dynamic environment. However, this ability that can learn adaptively in dynamic environment is very important for any intelligent systems. Deep neural networks have shown the best learning ability in many applications. However, when we apply these methods to incrementally update the model parameters, the model would face catastrophic interference or forgetting problems, which can cause the model to forget the old knowledge after learning a new task. The research of continual learning alleviates this problem. Continual learning is a process of simulating brain learning. It learns continual non-independent and identically distributed data streams in a certain order, and incrementally updates the model according to the results of task. The significance of continual learning is to efficiently transform and use the knowledge that has been learned to complete the learning of new tasks, and to greatly reduce the problems caused by forgetting. The study of continuous learning is of great significance for intelligent computing systems to adaptively learn changes in the environment. In view of the application value, theoretical significance and future development potential of continual learning, the article systematically reviews the research progress of continual learning. Firstly, this paper outlines the definition of continual learning. Three typical continual learning models are introduced, namely learning without forgetting, elastic weight consolidation and gradient episodic memory. Then, the key problems and solutions of continual learning are also introduced. After that, the three types of methods based on regularization, dynamic framework, memory replay and complementary learning systems have been introduced. At last, this paper points out potential challenges and future directions in the field of continual learning.
百度学术
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