  The pursuit of long-term autonomy mandates that machine learning models must
continuously adapt to their changing environments and learn to solve new tasks.
Continual learning seeks to overcome the challenge of catastrophic forgetting,
where learning to solve new tasks causes a model to forget previously learnt
information. Prior-based continual learning methods are appealing as they are
computationally efficient and do not require auxiliary models or data storage.
However, prior-based approaches typically fail on important benchmarks and are
thus limited in their potential applications compared to their memory-based
counterparts. We introduce Bayesian adaptive moment regularization (BAdam), a
novel prior-based method that better constrains parameter growth, reducing
catastrophic forgetting. Our method boasts a range of desirable properties such
as being lightweight and task label-free, converging quickly, and offering
calibrated uncertainty that is important for safe real-world deployment.
Results show that BAdam achieves state-of-the-art performance for prior-based
methods on challenging single-headed class-incremental experiments such as
Split MNIST and Split FashionMNIST, and does so without relying on task labels
or discrete task boundaries.
