Continual learning and its industrial applications: A selective review

In many industrial applications, datasets are often obtained in a sequence associated with a series of similar but different tasks. To model these datasets, a machine‐learning algorithm, which performed well on the previous task, may not have as strong a performance on the current task. When the architecture of the algorithm is trained to adapt to new tasks, often the whole architecture needs to be revised and the old knowledge of modeling can be forgotten. Efforts to make the algorithm work for all the relevant tasks can cost large computational resources and data storage. Continual learning, also called lifelong learning or continual lifelong learning, refers to the concept that these algorithms have the ability to continually learn without forgetting the information obtained from previous task. In this work, we provide a broad view of continual learning techniques and their industrial applications. Our focus will be on reviewing the current methodologies and existing applications, and identifying a gap between the current methodology and the modern industrial needs. This article is categorized under: Technologies > Artificial Intelligence Fundamental Concepts of Data and Knowledge > Knowledge Representation Application Areas > Business and Industry An illustration of continual learning method to overcome distribution shifting and catastrophic forgetting in a classification problem.