Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: This paper proposes to add group equivariance to the encoder of a hyperspherical variational auto-encoder to disentangle group transformations from image content. Results on disentangling rotation from an augmented MNIST dataset with hyperparameter search show the potential of this method. 
strengths_and_weaknesses: Strengths:
* Searching across multiple hyperparameter values strengthens the results, and provide evidence for the claims/analysis of the roles of these hyperparameters.
* Experimental details are provided in Appendix A, and it seems that code will be shared after de-anonymization.
* The paper is grammatically well-written overall.


Weaknesses:
* Discussion of relevant literature could be improved.
* The results are not presented very clearly, and need further analysis to be fully developed.
suggested_changes: The results are not very clear nor thoroughly analyzed. After explaining explicitly and/or labeling the top and bottom portions of Figure 1, what can we conclude from them? Why are the disentangled representations mostly properly oriented? Why are validation loss results presented in Table 1, when the hyperparameters were finally selected by performance in disentanglement and reconstruction? How are these latter qualities measured?

Some citations could be improved. What is the "β" of β-VAE? Davidson et al. (2018) should be cited immediately after naming S-VAEs. What is the comparison between your work and Vadgama et al. (2022)?

Sharvaree Vadgama, Jakub Mikolaj Tomczak, and Erik J. Bekkers. Kendall Shape-VAE: Learning Shapes in a Generative Framework. NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations. 2022.
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: This paper aims to disentangle image representations from rotations through the use of a group-equivariant neural network (G-CNN). It shows that image embedding agnostic to rotations can be naturally obtained by implementing directly a SO(2) equivariant CNN as the encoder of a S-VAE.
strengths_and_weaknesses: Strengths
- The paper introduces its objective clearly.
- The methodology and experiment are described in detail.

Weaknesses
- There is no comparison between the proposed VAE with G-CNN encoder and VAE without G-CNN encoder.
- The paper doesn’t try other G-CNN architectures or other VAE architectures.
suggested_changes: - Adding more evaluation metrics aside from loss could better represent the results.
- If there are some relevant previous works, it would be better to include them in the experimental result to show how the proposed method fares against them.
- Adding more datasets to experiment on would also show how the proposed method generalizes across tasks.
review_title: null

Meta-review: metareview: In this study, the authors aim to disentangle image content from its rotation, which is particularly desirable in various applications such as astronomical data and histopathological images. To achieve this goal, they propose the use of a Group Equivariant Convolutional Neural Network (G-CNN) encoder and demonstrate its effectiveness on the MNIST dataset.

confidence: 5: The area chair is absolutely certain
Decision: Invite to archive