Venue: ICLR 2022 Track Blog

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: The author provides a great overview of viewmaker networks, and then performed some interesting experiments to explore the phenomenon of dimensional collapse in learned representations. The additional SVD and CKA experiments are a very interesting direction to explore, but I share the concerns of reviewer i7Ai in that it is difficult to make meaningful conclusions without any notion of error bars given how minimal the differences are. I'm very positive on the ideas and the overall direction this post takes, but unfortunately the results are a bit too preliminary and inconclusive. This post would benefit greatly with a bit more refinement and care to ensure the empirical results are statistically significant, which would permit more confidence in the conclusions that are drawn. 
review_title: Well written overview and interesting direction, but seems a bit incomplete

final_score: rating: 7: Good paper, accept
review_content: ### Summary

This blog post summarizes the viewmaker[1] method for self-supervised settings. The authors present a good background on the effect of utilizing different data augmentation, i.e. view making, on the quality of learned representations. Later, they present some background on *adversarial perturbations* and how the viewmaker[1] paper uses norm constrained perturbations to generate a new view(augmented image) for the input image. Finally, the authors use a set of comparative experiments between the viewmaker method and SimCLR augmentations to study the dimensional collapse and representation similarity using SVD and CKA analysis.

### Strengths:

* The blog post is well-written and the authors decently utilize additional figures and plots if necessary.
*  The comparative analysis on dimensional collapse and feature similarity of ViewMaker and SimCLR augmentations are interesting and insightful.

### Concerns:

* About the SVD experiment, the conclusions do not strike me to be necessarily valid since the difference is quite marginal, and averaging the plot across several runs might give us almost similar results.
review_title: The post is well-written and the experiments are insightful, but some of the conclusions might not be necessarily valid

Meta-review: The post offers a good summary of the original ICLR paper and conducts some interesting follow up experiments, but we feel that the follow-up experiments are a bit too preliminary and would benefit with more discussion.
Decision: Reject