Weakness] 1. Although the paper argue that proposed method finds the flat minima, the analysis about flatness is missing. The loss used for training base model is the averaged loss for the noise injected models, and the authors provided convergence analysis on this loss. However, minimizing the averaged loss across the noise injected models does not ensure the flatness of the minima. So, to claim that the minima found by minimizing the loss in Eq (3), the analysis on the losses of the noise-injected models after training is required. 2. In Eq (4), the class prototypes before and after injecting noise are utilized for prototype fixing regularization. However, this means that F2M have to compute the prototypes of the base class every time the noise is injected: M+1 times for each update. Considering the fact that there are many classes and many samples for the base classes, this prototype fixing is computationally inefficient. If I miss some details about the prototype fixing, please fix my misunderstanding in rebuttal. 3. Analysis on the sampling times M
and noise bound value b
is missing. These values decide the flat area around the flat minima, and the performance would be affected by theses value. However, there is no analysis on M and b
in the main paper nor the appendix. Moreover, the exact value M
used for the experiments is not reported. 4. Comparison with single session incremental few-shot learning is missing. Like [42] in the main paper, there are some meta-learning based single session incremental FSL methods are being studied. Although this paper targets on multi-session incremental FSL with different setting and different dataset split, it would be more informative to compare the proposed F2M with that kind of methods, considering that the idea of finding flat minima seems valuable for the single session incremental few-shot learning task too.
There is a typo in Table 2 – the miniImageNet task is 5-way, but it is written as 10-way.
Post Rebuttal
Reviewer clarified the confusing parts of the paper, and added useful analysis during rebuttal. Therefore, I raise my score to 6.
1. Although the paper argue that proposed method finds the flat minima, the analysis about flatness is missing. The loss used for training base model is the averaged loss for the noise injected models, and the authors provided convergence analysis on this loss. However, minimizing the averaged loss across the noise injected models does not ensure the flatness of the minima. So, to claim that the minima found by minimizing the loss in Eq (3), the analysis on the losses of the noise-injected models after training is required.