PONE-D-23-01498Mitigating Carbon Footprint for Knowledge Distillation Based Deep Learning Model CompressionPLOS ONE

Dear Dr. Rahman,

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Süleyman Eken, Ph.D.

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Knowledge distillation based methods are very important nowadays due to providing an efficent computation for small hardware resources. The paper is written in this direction. In general, I like the paper including theoretical background and experimental results. Results are clear to prove their hypothesis. On the other hand, it would be better if the following revisions are applied in the next version of the manuscript.

1- There is another way to compress big DL model called model quantization/pruning. As is known, each parameter is stored in a 32-bit data structure. You can simply quantize/prune the model using the state-of-the-art techniques and compare them with knowledge distillation. It would increase the popularity of the paper.

2- There are no sample images so it is very hard to interpret results. You can extend the paper with the results and compare in terms of human vision.

Reviewer #2: The authors examined the environmental costs (carbon footprints) for deep learning model compression using knowledge distillation. The authors have extensively experimented with different combinations of student-teacher models based on the architectures of ResNet18, MobileNetV2 and ShuffleNetV2, as well as CIFAR10, CIFAR 100, Tiny Imagenet and PASCAL VOC reported both object recognition and detection problems using their datasets. Researchers have taken up a very interesting study indeed. My reviews and suggestions about their publications are listed;

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You should use a more academic language. The authors should polish the manuscript to improve its writing. The quality of the article should be increased. English must be strongly revised to make the paper even readable.

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Reviewer #1: No

Reviewer #2: No

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Mitigating Carbon Footprint for Knowledge Distillation Based Deep Learning Model Compression

PONE-D-23-01498R1

Dear Dr. Rahman,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Süleyman Eken, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I would like to thank the authors for their efforts on the last version. I think the manuscript is ready to meet with the readers.

Reviewer #2: The authors have progressed in improving the paper compared to previous versions of the paper (PONE-D-23-01498 & PONE-D-23-01498_R1). When the previous and revised versions of the paper are evaluated together, it is seen that the authors make the corrections requested by the referees and show the necessary sensitivity in the revision of the paper in line with the comments. In the revised version of the paper, almost all the comments have been considered and addressed by the authors.

The response to reviewers file is well-prepared. The changes made by the authors in line with the opinions/suggestions/evaluations of the referees can be tracked. The existing organization and spelling problems in the previous version of the article have been fixed. In the revised version, the clarity and follow-up of the study have been increased. In addition, the article has been carefully reviewed for grammatical and typos.

As a result, my concerns on the previous version of the paper have disappeared with the explanations made by the authors, as well as the revision they have made.

This revision is sufficient, and it is possible to evaluate the paper for publication after preparation according to PLOS ONE template.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

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