From: ICLR.cc/2021/Workshop/Neural_Compression/Program_Chairs
title: Paper Decision
decision: Accept
comment: While reviews were borderline, the program chairs decided to accept this paper as it presents an interesting application, enough empirical results for a workshop contribution, and it adds to the diversity of ideas presented at the workshop.

The second review could not be sent in due to technical problems and was instead sent in by email. Please find it below.

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SECOND REVIEW:

The paper considers the problem of lossily compressing data from a physics simulation. 
In particular, five-dimensional particle distribution data is considered using a a so called "F-data" representation.
Here the main object of interest is a a 2-D histogram array of size 39 x 39 which represents the distribution of velocities at a given 3-D space point 
in a physics experiment.

The authors apply a VQ-VAE on the 2-D histograms, to lossily compress them. The training objective is a combination of a) a MSE term, b) the VQ-VAE objective and c) a "physics loss".

The experimental evaluation consists of a qualitative/visual comparison on select examples (Figure 3) as well as a size and compression ratio comparison between the proposed method and the original data.

I think the problem is interesting and it is well motivated: these physics experiments generate massive amounts of data which neural compression might be well suited for. The quality of the writing is good but I found the paper quite lacking in details. However, I have some serious concerns about the current state of the work.

In particular:

Poor baseline: The baseline approach seems to be to lossleslly compress the data with GZip (first row  Tab. 1), achieving a compressed size of 54MB.
The proposed method seems to achieve a size of 94MB while also being lossy? What is going on here? Why is there no lossy baseline?

A reasonable weak baseline would be e.g. an image codec (say JPEG 2000 or BPG) applied to the data, which is very image-like in appearance.

Poor evaluation: A lossy compression method inherently has a trade-off between bitrate and quality/performance. This can be achieved by varying the quality parameter of classical methods (such as JPEG) and adjusting the loss terms for the neural methods. A proper evaluation needs to account for this trade-off and either ensure equal bitrates between methods or plot rate-distortion curves.

Disregard for existing literature: A much more reasonable baseline would be to use some standard neural compression approaches like "Variational image compression with a scale hyperprior" (Balle et al, 2018), and train it with respect to rate/distortion where distortion could include the "physics loss".

Final recommendation: based on the above, I give the paper the rating "borderline" (but "weak reject" if I had to choose between weak accept/reject).