Title: ImpressLearn: Continual Learning via Combined Task Impressions. Abstract: This work proposes a new method to sequentially train a deep neural network on multiple tasks without suffering catastrophic forgetting,  while endowing it with the capability to quickly adapt to unknown tasks.  Starting from existing work on network masking (Wortsman et al., 2020), we show that a simple to learn linear combination of a small number of task-specific masks (”impressions”) ona randomly initialized backbone network is sufficient to both retain accuracy on previously learned tasks, as well as achieve high accuracy on new tasks.

In contrast to previous methods, we do not require to generate dedicated masks or contexts for each new task, instead leveraging transfer learning to keep per-task parameter overhead negligible. Our work illustrates the power of linearly combining individual impressions, each of which fares poorly in isolation, to achieve performance comparable to a dedicated mask. Moreover, even repeated impressions from the same task (homogeneous masks), when combined can approach the performance of heterogeneous combinations if sufficiently many impressions are used.

Our approach  scales  more  efficiently  than  existing  methods,  requiring  orders  of magnitude fewer parameters and can function without modification even when task identity is missing.  In addition, in the setting where task labels are not given at inference, our algorithm gives an often favorable alternative to the entropy based task-inference methods proposed in (Wortsman et al., 2020).  We evaluate our method on a number of well known image classification data sets and architectures