PONE-D-24-42138Threshold-Based Exploitation of Noisy Label in Black-box Unsupervised Domain AdaptationPLOS ONE

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Academic Editor

PLOS ONE

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Additional Editor Comments:

The study shows promise, but certain aspects require further elaboration and clarification to enhance its rigor and impact. For example, the reviewers highlight the need for a more detailed analysis of hyperparameter sensitivity, particularly thresholds and regularization parameters critical to the method's performance. They recommend including explicit comparisons of computational efficiency against baseline methods to emphasize the practical benefits. Additionally, a discussion on potential failure cases and the method's generalizability to domains beyond image classification is necessary to clarify its broader applicability and limitations.

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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: Yes

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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: Yes

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: The authors proposed "Threshold-Based Exploitation of Noisy Label in Black-box Unsupervised Domain Adaptation". The structure of the article is well structured. But authors should follow the following comments.

1.Proofread the entire manuscript.

2.Draw a graphical abstract of your proposed approach.

3.Compare your approach with previous approaches.

4. Explain about features you are using in this study.

Reviewer #2: This paper tackles the significant challenge of Black-box Unsupervised Domain Adaptation (UDA), where source data and model parameters are inaccessible due to privacy constraints. The authors propose Threshold-Based Exploitation of Noisy Predictions (TEN), a method designed to adapt target models using noisy labels generated by a black-box source model. TEN introduces a flexible thresholding mechanism to classify data into clean and noisy subsets, effectively handling class imbalance and improving learning from high-confidence instances. It further employs knowledge distillation for clean labels, negative learning for noisy labels, and structural regularization techniques to enhance the adaptation process. Extensive experiments demonstrate that TEN achieves up to 9.49% higher accuracy than existing baselines, highlighting its robustness and practicality. The flexible threshold approach is particularly notable, addressing the imbalance and difficulty of learning from noisy labels effectively. Additionally, the integration of knowledge distillation, negative learning, and entropy regularization creates a well-rounded and efficient framework for improving target model performance. The authors support their claims with extensive experiments across multiple datasets and scenarios, including single-source and multi-source UDA, demonstrating consistent accuracy improvements. The inclusion of ablation studies further validates the significance of each component in the proposed method.

However, there are still some issues to be addressed before the paper is accepted:

1. There should be a deeper discussion of hyperparameter sensitivity, particularly the thresholds and regularization parameters critical to TEN's performance.

2. While the computational efficiency of the method is implied, explicit comparisons of overhead against baselines would strengthen the practical appeal.

3. The authors should discuss the potential failure cases or generalizability to domains beyond image classification limits the broader applicability of the findings.

4. The authors should include references related to UDA and black-box UDA in related work, such as [1-3].

[1] Zhang, J., Huang, J., Jiang, X., & Lu, S. (2023). Black-box unsupervised domain adaptation with bi-directional atkinson-shiffrin memory. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 11771-11782).

[2] Zhu, C., Wang, Q., Xie, Y., & Xu, S. (2024). Multiview latent space learning with progressively fine-tuned deep features for unsupervised domain adaptation. Information Sciences, 662, 120223.

[3] Zhu, C., Zhang, L., Luo, W., Jiang, G., & Wang, Q. (2024). Tensorial multiview low-rank high-order graph learning for context-enhanced domain adaptation. Neural Networks, 106859.

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Reviewer #1: No

Reviewer #2: No

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Threshold-Based Exploitation of Noisy Label in Black-box Unsupervised Domain Adaptation

PONE-D-24-42138R1

Dear Dr. Kang,

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,

Lei Chu

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 #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 #2: Yes

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

Reviewer #2: Yes

**********

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 #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 #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 #2: The authors have addressed my previous concerns. The manuscript is suggested to be accepted under current version.

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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 #2: No

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