Venue: ICLR 2022 Track Blog

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: ## Summary of the contribution

The blog post (BP) addresses the Co-Mod-GAN paper my Zhao et al, ICLR 2021. 
The paper highlights 2 contributions: a new information flow to improve GAN-based in-painting tasks for large missing areas, and a new SVM-based metric.
The BP provides an overview of the paper's contribution then goes on to provide additional experimental results based on similar setups, yet exploring concepts the paper did not directly mention: color diversity, shape bias and failure cases.

## Strengths

The BP provides a good summary of the paper, condensing its main contributions, splitting figures when necessary for the reader. It is concise and to the point, well illustrated. In addition to this pure transposition of the information already present in the paper the BP adds relevant additional experiments. I think it is especially interesting to explore the capabilities of a pre-trained model on different data. We all know using others' code can be challenging so it is valuable to show that it can be done (even if we have no idea how difficult that was for the BP's authors)\*. There is value in providing more related work than mentioned the paper, especially pointing to non-GAN-based strategies.

\* I may have misunderstood what has been done as it is not clear to me what comes from code, and what comes from the website accompanying the paper

## Weaknesses

The most important weakness I find in the BP is the lack of "personal" and new perspectives / reformulations / criticism. The BP reads like a summary and does not make the paper significantly more accessible to a wider audience. I believe (and so does the call for papers) that blog posts should be more than just the compression of a paper.

1. I find the introduction too abrupt, to delve directly in the topic (just like the paper, but it's expected there). In the context of a blog post I believe a section on GANs/Image-to-image translation in general is required, be it simply with illustrations. For instance the claim "Since vanilla GANs do not perform well in free-form image completion tasks" could be more accessible with a custom illustration or at least a figure from one of the references instead of just citing as a regular paper does.

2. Section 1., while summarizing the content of the paper, provides no additional value. There is no perspective given to why, in the BP authors' experience or knowledge, this or that choice was made (historical context, results / experiments / figures from other papers in the related work that the paper improves upon etc.). In addition, I would have liked the BP authors to provide more personal points of view on topics instead of "simply" (no offense) reproducing the paper's content. For instance: 

    1. "The linear mapping in the style vector contributes to stochasticity". This sentence feels arbitrary. I believe one of the paper's weaknesses lies in this affirmation that is not justified in any way either theoretically, or insightfully or even experimentally with an ablation study (it just *is*, because Figure 11 says so). The BP should, if not directly identify the weakness, at least provide an intuition for why that sentence may be true.

    2. The paper authors introduce a new metric, which includes a linear SVM. This could have been the opportunity for the BP authors to include a custom figure illustrating why that is a good choice or at least what it looks like. And if not with a figure, I think unpacking the formulae would greatly help the BP's pedagogy.

3. Section 2. I'm not sure I understand exactly the setup.

    1. "we first create a semantic segmentation, using the model trained on the MIT ADE20K dataset." -> What is "the model" ?

    2. "Using the co-modulated GAN pre-trained on the Places2 dataset" -> Is this using the code or the website?

    3. In any case, I totally agree diversity is not great, (cf the above criticism) but I don't agree with the criticism that the in-painted pool should have the mask's shape. The model is trained to in-paint *arbitrary* masks and therefore is encouraged, to the contrary of what you are implying, to *ignore* the shape of the mask to fill the void. It is *right* in my opinion to render a more rectangular pool because this is likely the most represented kind of pool in the dataset. *If* it had not collapsed and had shown diversity, I would have agreed that it should have displayed at least 1 sample with non-rectangular pools. But in the case of mode-collapse (as it seems to have happened) the rectangular shape is what I expect, the should not be expected to blindly follow the mask.

4. Section 3. This section is short and to the point but again lacks contribution in my opinion
    1. "As a result, strange artifacts may appear" ->  What are you talking about? I mean, exactly, what do you mean by "strange" and where should I look for? The next figure literally contains 15 images. Very simple annotations onto the figure (red circles for instance) and a more informative caption would help the reader a lot at low cost
    2. "As the mask [...] generated image to the input image" -> What is the criticism/limitation here? It reads like you are implying that the model should have been able to, somehow, infer those features from the context. This seems nearly impossible to me. So it has to sample from the multi-modal and high-dimensional distribution it has learned to fit, yielding expected variations in those features.

5. Section 4. I'm not sure why this section is there, and not merged with the introduction.

Overall, there are few things I consider to be wrong (actually none except the pool), but the only extra-value I get by reading the blog post instead of the paper, is time saved. This is fine as a reviewer familiar with GANs and applications to computer vision, I think it is not enough for a broader audience.

## Rating

As the expectations around a blog post may vary a lot and this is the first iteration of the workshop, I tried to identify main expected features of an ideal blog post from the call for paper and rate those individually before averaging.

* discussion about *ideas*: 3/10
* clarity: 8/10
* pedagogy: 5/10
* new theoretical or practical insights: 4/10
* reproduction/extension of experiments: 7/10

Rating: 5.4
review_title: A condensed version of the paper, with additional insights to capabilities and limitations 

Meta-review: The reviewer pointed out the lack of added value with respect to the original paper (either discussion about ideas or experiments)
Decision: Reject