Venue: ICLR 2022 Track Blog

Reviews:

final_score: rating: 3: Clear rejection
review_content: This blog post reviews the ICLR 2019 oral: ‘ImageNet-trained CNNs are biased towards texture’.
The introduction of the blog posts motivates the study very well, and even though some claims are left uncited, it clearly states the paper’s goals. It then proceeds to summarize literature that has previously touched on the topic of texture bias in CNNs with quotations, and afterwards it proceeds to describe the experimental framework of the paper. Then, it describes the tasks, presents a solution to texture bias, and empirical results comparing human judgement with learned classifiers. Overall, the reviewer finds the blog post’s writing difficult to parse, and it would improve a lot by restructuring some sections and fixing typos, as well as grammatical errors, which are abundant in the document.

The blog post would improve by taking the following suggestions in consideration:
* It is not clear how the paper examined departs from previous literature, since all of the listed papers seem to agree that texture is learnt easier than shape, or that classifying in absence of texture cues is difficult.
* In the ‘Psychophysical Experiment’ section, there could be a methodology section explaining how the human judgements would be compared to the trained classifiers, and how the quantities of interest in the plots are derived.
* Structurally, it would be better to present the negative results about `the shape vs texture bias in CNNs and in humans` before introducing the solution which closes the discrepancy between the two; that is the ‘Stylized Imagenet’. This way the solution is motivated better, and it makes the blog post easier to read.
* It is good practice to provide with citations (or at least links in the blog post case) which point to work supporting claims regarding literature or methods. Saying that, I find there are many citations missing from the body of the blog post. For instance: in the introduction mentioning methods and applications of could come along with some citations of seminal work or technical reports. The same applies later in the text when definitions from the literature are mentioned (such as ‘black box’ attack at the end of introduction, and ‘adversarial transferability’ at the end of conclusion).

Strengths and Weaknesses  
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\+ Overall accurate representation of the paper  
\+ Cross-references another blog post (which is nice)  
\- However, ideas are not presented with clarity most of the times  
\- Does not contribute further insight about the work presented  

Decision recommendation  
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I find that the blog post requires major changes, thus I recommend rejection.
review_title: Nice attempt of capturing the ideas presented, but writing is not clear

Decision: Reject