Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The proposed work describes the "Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization." Although the authors have covered all the aspects broadly and explained them appropriately, the following modifications need to be considered for possible publications;

1. The introduction needs to be more elaborative. The introduction must contain the background of the study, the significance of the study, present research trends, and the impact of the present research domain on the markets with some facts and figures. 

2. Highlight the major research gap in the existing anomaly detection methods and the novelty of the proposed work in overcoming the limitations. 

3. Table 3-5: The text "Best results are highlighted in bold" should be at the footnote of each table. 

4 It will be more effective if the proposed approach can be implemented on the real-time dataset collected by real-time image acquisition and prove its effectiveness. 

Comments on the Quality of English Language

Minor English changes require some sentence corrections; otherwise, the writing is excellent. 

Author Response

Please see the attachment

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

In this paper, A reconstruction-based strategy using the noise-to-norm paradigm is proposed to avoid invariant reconstruction of anomalous regions. The  M-net-based reconstruction network uses multiscale fusion and residual attention modules for complete anomaly identification and localization.

This well-written paper has several interesting points and a relevant topic. The article should be rewritten in proper English. Several fundamental ideas are poorly expressed and described. Some sentence samples need revision.

Comments and Suggestions for Authors

1.      The paper must be proofread. Remove typos and grammatical and linguistic errors. Grammar and English must be improved.

2.      I suggest that the major contributions in this work should be listed at the end of the introduction instead of paragraphs.

3.      Show explicitly what is “novel” in the method you proposed.

4.      Authors must demonstrate how their approach outperforms others, such as comparative analysis.

5.      What are the benefits of anomaly detection in manufacturing?

6.      How do you know when an anomaly is real versus just a data error?

7.      I suggest adding enough content added. e.g., introduction and discussion sections.

 

 

Comments for author File: Comments.pdf

Comments on the Quality of English Language

Author Response

Please see the attachment

Author Response File: Author Response.docx