- Lack of comparison with a highly relevant method. [1] also proposes to utilize the previous knowledge with ‘inter-task ensemble’, while enhancing the current task’s performance with ‘intra-task’ ensemble. Yet, the authors didn’t include the method comparison or performance comparison.
- Novelty is limited. From my perspective, the submission simply applies the existing weight averaging and the bounded update to the class incremental learning problems.
- No theoretical justification or interpretation. Is there any theoretical guarantee that to what extent inter-task weight averaging or bounded update counter catastrophic forgetting?
- Though it shows in Table 3 that bounded model update mitigates the forgetting, the incorporation of bounded model update doesn’t seem to have enough motivation from the methodological perspective. The inter-task weight average is designed to incorporate both old and new knowledge, which is by design enough to tackle forgetting.
##### References:
1.Miao, Z., Wang, Z., Chen, W., & Qiu, Q. (2021, October). Continual learning with filter atom swapping. In International Conference on Learning Representations.
- Lack of comparison with a highly relevant method. [1] also proposes to utilize the previous knowledge with ‘inter-task ensemble’, while enhancing the current task’s performance with ‘intra-task’ ensemble. Yet, the authors didn’t include the method comparison or performance comparison.