We present a framework for interpretable continual learning (ICL). We show that explanations of previously performed tasks can be used to improve performance on future tasks. ICL generates a good explanation of a finished task, then uses this to focus attention on what is important when facing a new task. The ICL idea is general and may be applied to many continual learning approaches. Here we focus on the variational continual learning framework to take advantage of its flexibility and efficacy in overcoming catastrophic forgetting. We use saliency maps to provide explanations of performed tasks and propose a new metric to assess their quality. Experiments show that ICL achieves state-of-the-art results in terms of overall continual learning performance as measured by average classification accuracy, and also in terms of its explanations, which are assessed qualitatively and quantitatively using the proposed metric.
Summary:
In this paper, the authors propose a framework for continual learning based on explanations for performed classifications of previously learned tasks. In this framework, an average saliency map is computed for all images in the test set of a previous task to identify image regions, which are important for that task. When learning the next task, this average saliency map is used in an attention mechanism to help learning the new task and to prevent catastrophic forgetting of previously learned tasks. Furthermore, the authors propose a new metric for the goodness of a saliency map by taking into account the number of pixels in the map, the average distance between pixels in the map, as well as the prediction probability given only the salient pixels.
The authors report that their approach achieves the best average classification accuracy for 3 out of 4 benchmark datasets compared to other state-of-the-art approaches.
Relevance:
This work is relevant to researchers in the field of continual/life-long learning, since it proposes a framework, which should be possible to integrate into different approaches in this field.
Significance:
The proposed work is significant, since it explores a new direction of using learner generated, interpretable explanations of the currently learned task as help for learning new tasks. Furthermore, it proposes a new metric for the goodness of saliency maps.
Soundness:
In general, the proposed approach of using the average saliency map as attention mask for learning appears to be reasonable. However, the following implicit assumptions/limitations of the approach should be made more clear:
	- important features for the new task should be in similar locations as important features of the old task (for example, one would expect that the proposed approach would negatively affect learning the new task if the important features of the old task were all located in the bottom of the image, while all important features for the new task are in the top)
	- the locations for important features should be comparatively stable (for example, one would expect the average saliency map to become fairly meaningless if important features, such as the face of a dog, can appear anywhere in the image. Therefore, an interesting baseline for the evaluation of the ICL approach would be a predefined, fixed attention map consisting of concentric circles with the image center as their center, to show that the proposed approach does more than just deemphasizing the corners of the image)
Furthermore, the authors appear to imply that increased FSM values for an old task after training on a new task indicate catastrophic forgetting. While this is a reasonable assumption, it does not necessarily seem to be the case that a larger, more disconnected saliency map indicates worse classification performance. Comparatively small changes in FSM may not affect the classification performance at all, while larger changes may not necessarily lead to worse classifications either. For example, by increasing the amount or size of image regions to be considered, the classifier may accidentally become more robust on an old task. Therefore, it may be a good idea for the authors to analyze the correlation between FSM changes and accuracy changes.
Evaluation:
The evaluation of the proposed approach on the four used datasets appears to be reasonable and well done. However, given that the achieved performance gains over the state-of-the-art are fairly small, it would be good to assess if the obtained improvements are statistically significant. 
Furthermore, it may be informative to show the saliency maps in Figure 5 not only for cases in which the learner classified the image correctly in both time steps, but also cases in which the learner classified the image correctly the first time and incorrectly the second time. Additionally, the previously mentioned evaluation steps, i.e., using a fixed attention map as baseline for the evaluation and evaluating the correlation between FSM and accuracy may be informative to illustrate the advantages of the proposed approach.
Clarity:
The paper is clearly written and easy to follow. One minor issue is that the first sentence of the third paragraph in Section 4 is not a full sentence and therefore difficult to understand.
Furthermore, on page 6, it is stated that the surrounding square $\hat{x}_i$ is 15 x 15 pixels, while the size of the square $x_i$ is 10 x 10. This appears strange, since it would mean that $x_i$ cannot be in the center of $\hat{x}_i$.