Continual Learning with Filter Atom Swapping

Continual learning is widely studied in recent years to resolve the \textit{catastrophic forgetting} of deep neural networks. In this paper, we first enforce a low-rank filter subspace by decomposing convolutional filters within each network layer over a small set of filter atoms. Then, we perform continual learning with filter atom swapping. In other words, we learn for each task a new filter subspace for each convolutional layer, i.e., hundreds of parameters as filter atoms, but keep subspace coefficients shared across tasks. By maintaining a small footprint memory of filter atoms, we can easily archive models for past tasks to avoid forgetting. The effectiveness of this simple scheme for continual learning is illustrated both empirically and theoretically. The proposed atom swapping framework further enables flexible and efficient model ensemble with members selected within task or across tasks to improve the performance in different continual learning settings. The proposed method can be applied to a wide range of optimization schemes and convolutional network structures. Being validated on multiple benchmark datasets, the proposed method outperforms the state-of-the-art methods in both accuracy and scalability.