Venue: ML_Reproducibility_Challenge 2020

Reviews:

final_score: rating: 7: Good paper, accept
review_content: This paper attempts to replicate the work in Interpretable Complex-Valued Neural Networks For Privacy Protection which in general was not possible due to missing details in the original paper.

The author provided a good description of the work that they have done to replicate the original. The authors identified the details that were missing in the paper. They also mention time constrains, which did not allow them to pursue for example some further hyperparameter tuning.

The authors tried to contact the original authors without any success.

What would have been interesting is to provide a full summary of what would have made the reproduction easier, besides providing the pseudo-code.

All-in-all, it is a good paper.

review_title: Accept

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: The authors did well. Though the codes for the original work was not available, they reproduce the work by coding from scratch.
review_title: A very good work

final_score: rating: 7: Good paper, accept
review_content: Even though the authors failed to reproduce results regarding performance, they find that the privacy claim is valid. 

They provide a detailed discussion about what was easy and what was difficult when they prepare the reproducibility report. The report is well-written and easy to follow. 
review_title: Good reproducibility report

Meta-review: The report is not anonyzimed.
Also, while it is well-written, it doesn't go into as much depth and detail as necessary to understand the results.
Decision: Reject