Recasting Continual Learning as Sequence Modeling
This work addresses the challenge of continual learning for AI systems by proposing a novel connection to sequence modeling, which is incremental in applying existing models to a new framework.
The paper tackles the problem of continual learning by reformulating it as a sequence modeling task, enabling the use of advanced sequence models like Transformers, and demonstrates competitive performance on seven benchmarks for classification and regression.
In this work, we aim to establish a strong connection between two significant bodies of machine learning research: continual learning and sequence modeling. That is, we propose to formulate continual learning as a sequence modeling problem, allowing advanced sequence models to be utilized for continual learning. Under this formulation, the continual learning process becomes the forward pass of a sequence model. By adopting the meta-continual learning (MCL) framework, we can train the sequence model at the meta-level, on multiple continual learning episodes. As a specific example of our new formulation, we demonstrate the application of Transformers and their efficient variants as MCL methods. Our experiments on seven benchmarks, covering both classification and regression, show that sequence models can be an attractive solution for general MCL.