There are some concerns regarding the method description and designs: 1) As described in the ASR update strategy, the rehearsal samples for previous tasks are based on the samples with high and low AS scores (the samples with middle AS scores are discarded) while for the current task the rehearsal samples are uniformly sampled from the corresponding data stream sorted by AS scores. Such difference between previous tasks and current task should be experimentally verified (for instance, why can't the current task follow the same principle as the previous tasks to select the rehearsal samples); 2) In line 5 of Algo.1, should it be noted that B^i_{t-}1 is already sorted according to the AS?
There are other concerns regarding the experimental settings and results: 1) As mentioned in Sec. 4.2, the mixup technique in LUMP is also adopted for the proposed method in the experiments on SplitCIFAR-100 and SplitTiny-ImageNet, there should be experimental results of excluding such mixup technique from the proposed method in order to demonstrate its pure contribution; 2) In order to better demonstrate the contribution of using ASR to update the reply buffer and its generalizabilty, there should be experiments of replacing the rehearsal buffer update strategy in the related works (for both supervised continual learning and continual self-supervised learning baselines that also adopt rehearsal buffer) by the proposed ASR update strategy.
The idea behind "augmentation stability of each sample is positively correlated with its relative position in corresponding category distribution" is not well proven or verified. Though Fig.1 tries to serve such purpose to show such idea, but it is not enough, can the authors provide more solid discussion or even theoretical proof for such idea if it is possible? Moreover, currently there is a hidden assumption that the distribution of each category is single mode, but how if the category distribution is multi-modal (in which it is very likely to happen in more complicated datasets), will AS still be effective as a proxy to the relative positive in a category distribution?
From my own research experience, for supervised continual learning, different strategies of rehearsal example selection (e.g. random or uniform) do not contribute significant difference to the final performance, can authors provide more discussion on the impact of particularly having both representative and discriminative rehearsal samples to the overall performance?
1) As mentioned in Sec. 4.2, the mixup technique in LUMP is also adopted for the proposed method in the experiments on SplitCIFAR-100 and SplitTiny-ImageNet, there should be experimental results of excluding such mixup technique from the proposed method in order to demonstrate its pure contribution;