I see in this paper are: - Although there is a clear and formal explanation of why it is not possible to discriminate among classes from different task when there is no access to data from those previous classes, I am not fully convinced that the set of parameters kept from previous classes, and used in regularization-based approaches, do not represent to some extent this data. In particular, there is no clear argument for the claim on page 5: “However, by hypothesis, \omega_{t-1} does not model the data distribution from C_{t-1} and therefore it does not model data distribution from C_{t-1} classes.”. I would like to see some discussion regarding how fairly a set of parameters \theta_{t-1} would represent the S’ set. - In terms of the experiments, I consider the number of tasks quite limited. To be convinced I would like to see several tasks (at least 10) and sequential results in terms of tasks learned rather than epochs.
Questions for authors:
Please address my comments on the weaknesses above.
- In terms of the experiments, I consider the number of tasks quite limited. To be convinced I would like to see several tasks (at least 10) and sequential results in terms of tasks learned rather than epochs. Questions for authors: Please address my comments on the weaknesses above.