Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Retraction

Retraction: Dual generative adversarial networks based on regression and neighbor characteristics

  • The PLOS One Editors

The PLOS One Editors retract this article [1] due to concerns about:

  • Compliance with PLOS policies on Submission and Publication of Related Studies.
  • Similarities to a previous publication [2].
  • The use of terminology that differs from standards in the field (e.g. “deep gaining knowledge” instead of “deep learning”).
  • The reliability of the reported results and conclusions.

All authors agreed with retraction.

The retracted article [1] was removed from the PLOS One website at the time of retraction due to the similarities with [2]. The article’s Copyright and Data Availability statements were also updated at the time of retraction, and the removed contents are no longer offered under the Creative Commons Attribution License.

References

  1. 1. Jia W, Lu M, Shen Q, Tian C, Zheng X. Dual generative adversarial networks based on regression and neighbor characteristics. PLoS One. 2024;19(1):e0291656. pmid:38236899
  2. 2. Tian H, Tian C, Li K, Jia W. Unbalanced regression sample generation algorithm based on confrontation. Inf. Sci. 2023;642:119157.

Citation: The PLOS One Editors (2025) Retraction: Dual generative adversarial networks based on regression and neighbor characteristics. PLoS One 20(5): e0324676. https://doi.org/10.1371/journal.pone.0324676

Published: May 15, 2025

Copyright: © 2025 The PLOS One Editors. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.