Venue: ICLR 2022 Workshop DGM4HSD

Reviews:

final_score: rating: 4: Ok but not good enough - rejection
review_content: This paper proposes a seq2seq model to learn damage representations with the goal of distinguishing different damage states. The overall application seems of practical use, but the method and analysis seems very premature. 

Many areas are not clear:
- The details of the model are not clear. RNN, LSTM, or GRU? Was the baseline model optimized for this task, i.e. with dropout, etc, or is it just an arbitrary baseline?
- It's not clear what the white noise test data is? What is the format of this? What does it measure? Perhaps some annotation within fig 2 could help understand what signal should be captured from this dataset. 
- What was the window size of the MLP? Just encoding 1 signal value? Was context included? If not, this seems to be a poorly chosen baseline model. Perhaps a convolutional kernel would help provide at least some local context for the embedding?
- It's not clear how good the seq2seq model is. It looks like it could just be phased by 1 time point. The analysis should be more quantitative instead of qualitative. A better baseline should be used, such as traditional time series modeling -- i.e. an exponentially weighted moving average and/or something a little more sophisticated like a Kalman filter.
- It is nice that the representation is well-separated, but it's not clear whether this is for the training set or test set. It would be more compelling if the representations were well separated on a held-out test set. 

In summary, the analysis of this paper is very premature and not compelling. I suppose the application could be of interest to the community, but as it stands, the work is not really in a state to be presented with any meaningful findings.

minor comment: 
- This is worded very confusingly: "We train the model to learn damage representations with only undamaged signals by feeding damaged signals into models." 
- in the future, the authors should withhold putting identifiable github links to code


review_title: A premature study of a seq2seq model for damage detection of buildings

final_score: rating: 5: Marginally below acceptance threshold
review_content: ## Summary

This paper presents a Seq2Seq model to reconstruct building damage sensor signals. Experiments provide some promising results regarding the signal reconstruction quality.

## Strengths 

S1: Applying deep learning to building damage detection with sensor data is very interesting. It can be a very useful application domain.

S2: Experiments (especially Fig 2) show some potentially promising results of this approach.

## Major weaknesses

W1: It is unclear what "damage signal" is from the abstract. Does it refer to natural images, languages, or audio signals?

W2: One might argue that the current introduction is too abstract. It would be much stronger to give more details about the Seq2Seq approach (e.g., model architecture, how is damaged signals used, result highlights, differences from other related works).

W3: Section 2 is not very clear. It seems this model uses sensor signals as input. This section would be stronger if it can better explains how the input signal is used, and how undamaged signal reconstruction can help building damage detection.

W4: Section 3 would be stronger if it presents quantitative results and explains how signal reconstruction is helpful for the end-task.

## Minor weaknesses

M1: The GitHub repo link in the abstract violates the anonymity policy. Please be careful for future submissions.
review_title: Interesting application domain while the paper presentation can be improved

Meta-review: Both reviewers agreed on the practical use of the studied problem but raised concerns about the clarity and presentation of the paper. The AC encourages the authors to take into account the review's suggestions and resubmit to a future venue.
Decision: Reject