Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Needs Clarification (NC): a submission which does not meet the reviewing criteria and needs clarification for its described problem or solution
review_content: summary_of_contributions: This work seeks to evaluate the performance of deep generative models for the anomaly detection task. 
strengths_and_weaknesses: Strengths:

- An interesting area that could use more fair evaluation (especially on clear tasks)

Weaknesses:

- The results are not included in this document
- The format requirements are not met
suggested_changes: - I would suggest including a table of the results in this work. I would be interested in what was found
- I would be interested in more details on the models and how they were implemented. There are a number of interesting method of anomaly detection. One thing that was not clear to me was what flavor of anomaly detection this paper considers. It seems like the one-class (or unsupervised) version of anomaly detection is meant but it would be great to clarify this.
- Note that this work is supposed to be anonymized (no author name included).
review_title: null

final_score: rating: Needs Clarification (NC): a submission which does not meet the reviewing criteria and needs clarification for its described problem or solution
review_content: summary_of_contributions: The authors look to compare different types of deep generative models as applied to unsupervised anomaly detection.
strengths_and_weaknesses: Strengths:
- Interesting idea.

Weaknesses:
- No figures/tables provided to validate results.
suggested_changes: Adhere to specified format and double-blind instructions.
review_title: null

Meta-review: metareview: Thank you for your submission. As the reviewers have noted, the premise of the paper is interesting and worth exploring. However, there are aspects of the paper which require further attention. First, all submission guidelines must be met: please use the provided Latex template for formatting, remove the author names, and include the URM statement. Second, as the reviewers described, it would increase the clarity and reproducibility of the work, as well as allow us to better assess the correctness, if results from your experiments can be provided, along with details about the experimental setup and task. 
confidence: 5: The area chair is absolutely certain
Decision: Invite to revise