深度模型的持续学习综述：理论、方法和应用

Biological organisms in nature are required to continuously learn from and adapt to the environment throughout their lifetime. This ongoing learning capacity serves as the fundamental basis for the biological learning systems. Despite the significant advancements in deep learning methods for computer vision and natural language processing, these models often encounter a serious issue, known as catastrophic forgetting, when learning tasks sequentially. This refers to the model’s tendency to discard previously acquired knowledge when acquiring new information, which greatly hampers the practical application of deep learning models. Thus, the exploration of continual learning is paramount for enhancing and implementing artificial intelligence systems. This paper provides a comprehensive survey of continual learning with deep models. Firstly, the definition and typical settings of continual learning are introduced, followed by the key aspects of the problem. Secondly, existing methods are categorized into four main groups: regularization-based, replay-based, gradient-based and structure-based approaches, with an outline of the strengths and weaknesses of each group. Meanwhile, the paper highlights and summarizes the theoretical progress in continual learning, establishing a crucial nexus between theory and methodology. Additionally, commonly used datasets and evaluation metrics are provided to facilitate fair comparisons among these methods. Finally, the paper addresses current issues, challenges and outlines future research directions in deep continual learning, taking into account its potential applications across diverse fields.