Weakness
- I do not see how MFN 'largely outperforms' existing baseline methods. It is difficult to identify the quality difference between output from the proposed method and SIREN -- shape representation seems to even prefer SIREN's results (what is the ground truth for Figure 5a). The paper is based on the idea of replacing compositional models with recursive, multiplicative ones, though neither the theory nor the results are convincing to prove this linear approximation is better. I have a hard time getting the intuition of the advantages of the proposed method.
- this paper, and like other baselines (e.g. SIREN) do not comment much on the generalization power of these encoding schemes. Apart from image completion, are there other experiments showing the non-overfitting results, for example, on shape representation or 3D tasks?
- the proposed model has shown to be more efficient in training, and I assume it is also more compact in size, but there is no analysis or comments on that? Suggestions
- Result figures are hard to spot differences against baselines. It's recommended to use a zoom or plot the difference image to show the difference.
- typo in Corollary 2 -- do you mean linear combination of Gabor bases?
- It's recommended to add reference next to baseline names in tables (e.g. place citation next to 'FF Positional' if that refers a paper method)
- In Corollary 1, $\Omega$ is not explicitly defined (though it's not hard to infer what it means).
- It's recommended to add reference next to baseline names in tables (e.g. place citation next to 'FF Positional' if that refers a paper method) - In Corollary 1, $\Omega$ is not explicitly defined (though it's not hard to infer what it means).