  A key challenge in the continual learning setting is to efficiently learn a
sequence of tasks without forgetting how to perform previously learned tasks.
Many existing approaches to this problem work by either retraining the model on
previous tasks or by expanding the model to accommodate new tasks. However,
these approaches typically suffer from increased storage and computational
requirements, a problem that is worsened in the case of sparse models due to
need for expensive re-training after sparsification. To address this challenge,
we propose a new method for efficient continual learning of sparse models
(EsaCL) that can automatically prune redundant parameters without adversely
impacting the model's predictive power, and circumvent the need of retraining.
We conduct a theoretical analysis of loss landscapes with parameter pruning,
and design a directional pruning (SDP) strategy that is informed by the
sharpness of the loss function with respect to the model parameters. SDP
ensures model with minimal loss of predictive accuracy, accelerating the
learning of sparse models at each stage. To accelerate model update, we
introduce an intelligent data selection (IDS) strategy that can identify
critical instances for estimating loss landscape, yielding substantially
improved data efficiency. The results of our experiments show that EsaCL
achieves performance that is competitive with the state-of-the-art methods on
three continual learning benchmarks, while using substantially reduced memory
and computational resources.
