1. The paper is lacking a clear and precise definition of unlearning. Its is important to show the definition of unlearning that you want to achieve through your algorithm.
2. The proposed algorithm is an empirical algorithm without any theoretical guarantees. It is important for unlearning papers to provide unlearning guarantees against an adversary.
3. The approach is very similar to this method (http://proceedings.mlr.press/v130/izzo21a/izzo21a.pdf) applied on each layer, which is not cited.
4. A simple baseline is just applying all the unlearning algorithm mentioned in the paper to the last layer vs the entire model. This comparison is missing.
5. All the unlearning verification are only show wrt accuracy of the model or the confusion matrix, however, the information is usually contained in the weights of the model, hence other metrics like membership attack or re-train time after forgetting show be considered.
6. The authors should also consider applying this method a linear perturbation of the network, as in those settings you will be able to get theoretical guarantees in regards to the proposed method, and also get better results.
7. Since the method is applied on each layer, the authors should provide a plot of how different different weights of the model move, for instance plot the relative weight change after unlearning to see which layers are affected the most after unlearning.
7. Since the method is applied on each layer, the authors should provide a plot of how different different weights of the model move, for instance plot the relative weight change after unlearning to see which layers are affected the most after unlearning.