Venue: ICLR 2022 Track Blog

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: ## Summary of the contribution

The Blog Post (BP) offers a summary of the paper "Do 2D GANs Know 3D Shape? Unsupervised 3D Shape Reconstruction from 2D Image GANs" (GAN2Shape) by Pan et al. It starts by providing introductory context required to get into the core idea of GAN2Shape then delves into the paper's core contribution: the 3-steps refinement cycle to leverage a pre-trained StyleGAN2 Generator in order to learn a 3D representation from a 2D image. At each step, the BP provides a summary of the algorithm and a quick description of relevant code functions in the original authors' code. Finally, they provide a Google Colab notebook to reproduce some results.

## Strengths 

One of the core value propositions of blog posts is to contextualize a research article. Authors of the BP manage to provide useful, accessible information on some of the most relevant topics required to understand the paper.

Additionally, while the paper already isolates quite clearly the 3 steps, the BP manages to improve on this structure by splitting the main figure and vulgarizing each core ideas.

The idea of commenting the code by linking step-wise ideas to actual functions is pretty helpful for a reader who'd want to reproduce results and get more practical about the paper's ideas and implementations. I would have liked to be able to run the Notebook myself, unfortunately the provided link (https://iclr.iro.umontreal.ca/0a0d7c4d-a3e2-413f-b9da-d9e5b06d97c0_1642139457/_includes/2022-01-13-gan2shape/GAN2Shape_Colab_Demo_Modified.html) does not work. This may not be the authors' fault but due to the anonymization procedure (?). If the Notebook provides further insights I will re-evaluate my ratings. 

It is useful to see that the paper's results are reproducible.

All in all, the blog post is easy to read and follow, presents the core algorithm clearly and is faithful to the original research. It provides a good summary of the paper's core contribution and some practical pointers.

## Weaknesses

There are 2 things I feel are not well explained:
* _step 2_: It is unclear from the BP alone what $w$ is, where it comes from, and that $E(I)$ actually predicts a $\Delta$ from $w$ (hence $G(E(I) + w)$
* _inference_: the training algorithm is quite complex and it's easy to lose the big picture. A description of the actual inference procedure would greatly help (for instance: why do we even _need_ $E$?)

There are no obvious mistakes in the BP and the following weaknesses are somewhat subjective.

### Missing notions

BP Authors made an effort to contextualize the paper and provide introductory material. I felt like some are still missing to make it accessible to a wider audience: albedo, renderer, L1 feature loss etc. Not all terms need to be defined, there's always a tradeoff but I think for a BP, that tradeoff could be a little lower, at a small cost.

### Personal point of view

I would have liked to read more personal insights in the BP's text. Providing a synthesis of a paper definitely _is_ a BP's goal, but far from the only one. Putting ideas in perspective is also a feature useful to a reader unfamiliar with the niche of GAN-based 3D reconstructions of 2D images. Beyond rephrasing what is in the paper, the BP's authors rarely provide their own opinion/review of ideas and procedures provided in the paper.

### Evaluation

A major point of research in image generation is to come up with evaluation strategies. It is not enough to say "I have a great architecture and I've looked at some samples and they seem better". I believe the paper's authors did try and provide both qualitative and quantitative evaluations and I think it should not be absent of the BP. It makes this step look secondary while it is core to any scientific contribution.

### Code

I think commenting code is a great idea. It just feels a little artificial when it's not put into a wider perspective: commenting on function names is a good start but including code extracts or pseudo-code to show _how_ those are used would be even more useful. Either through code blocks or embedding Github Gists.

### Experiments

It's great that the paper could be reproduced. As I cannot see the Notebook, the following ask may be too much work but an interesting contribution of the BP could have been to change the data. Looking at the limits of the network. Or test different hyper-parameters. For instance, playing with the number (4 in the paper) of cycles per image is something that seems like a low hanging fruit to produce new practical insights.

## 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*: 4/10 
* clarity: 7/10 
* pedagogy: 8/10 
* new theoretical or practical insights: 4/10
* reproduction/extension of experiments: 6/10 

Rating: 5.8
review_title: A review of the GAN2Shape Blog Post

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