Venue: ICLR 2022 Track Blog

Reviews:

final_score: rating: 4: Ok but not good enough - rejection
review_content: This blog discusses the paper “MODALS: Modality-agnostic Automated Data Augmentation in the Latent Space”, which proposes a modality agnostic framework that does not use any domain knowledge. The framework first learns a latent space from the data and then do transformations in the latent space. 

The blog summarizes the MODALS paper in a clean and concise way. In particular, the authors provide some illustrations of the transformations in the latent space, which can help the readers better understand the transformation algorithms. 

The main shortcoming of the blog is that it does not bring much new insight. Most of what is written in the closing thoughts section has already been discussed in the original paper. I think the authors could expand the closing thoughts section to include more details and new insights. For example, the authors mentioned there is potential to extend MODALS to other tasks like regression, but did not give any discussion on how MODALS can be extended.
review_title: Blog Review

Decision: Reject