Learning to Prompt for Continual Learning

Posted by Zifeng Wang, Student Researcher, and Zizhao Zhang, Software Engineer, Google Research

Supervised learning is a common approach to machine learning (ML) in which the model is trained using data that is labeled appropriately for the task at hand. Ordinary supervised learning trains on independent and identically distributed (IID) data, where all training examples are sampled from a fixed set of classes, and the model has access to these examples throughout the entire training phase. In contrast, continual learning tackles the problem of training a single model on changing data distributions where different classification tasks are presented sequentially. This is particularly important, for example, to enable autonomous agents to process and interpret continuous streams of information in real-world scenarios.

To illustrate the difference between supervised and continual learning, consider two tasks: (1) classify cats vs. dogs and (2) classify pandas vs. koalas. In supervised learning, which uses IID, the model is given training data from both tasks and treats it as a single 4-class classification problem. However, in continual learning, these two tasks arrive sequentially, and the model only has access to the training data of the current task. As a result, such models tend to suffer from performance degradation on the previous tasks, a phenomenon called catastrophic forgetting.

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