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Editorial Note

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Editorial Note: Data augmentation-assisted deep learning of hand-drawn partially colored sketches for visual search

  • The PLOS One Editors

Notice of Republication

The PLOS One Editors issue this Editorial Note to inform readers that an undisclosed competing interest between the Academic Editor and the authors was identified after the publication of [1].

PLOS reviewed this matter and concluded that the publication of [1] is supported based on independent expert input that was not affected by the competing interest concerns. PLOS regrets that the issues were not addressed prior to the publication of [1].

This article [1] was republished on February 25, 2026, to replace Figures 1-3, 7, 9, and 10 due to a permissions issue. The original published versions of these figures included content that was published previously by other sources [2–4] which include images that are not available under a CC BY license. The republished article also includes new S1-S2 Files. Please download this article again to view the correct version.

References

  1. 1. Ahmad J, Muhammad K, Baik SW. Data augmentation-assisted deep learning of hand-drawn partially colored sketches for visual search. PLoS One. 2017;12(8):e0183838. pmid:28859140
  2. 2. Çalışır F, Ulusoy O, Güdükbay U, Muhammet B. Mobile Multi-View Object Image Search – Dataset [Internet]. 2015 [cited 2026 January 23]. Available from: https://www.cs.bilkent.edu.tr/~bilmdg/mvod/
  3. 3. Wang JZ. Download 10,000 test images (low resolution web-crawled misc database used in WBIIS) [Internet] [cited 2026 January 23]. Available from: https://wang.ist.psu.edu/docs/home.shtml#download
  4. 4. Griffin G, Holub A, Perona P. Caltech 256 (1.0) [Data set]. CaltechDATA. 2022.

Citation: The PLOS One Editors (2026) Editorial Note: Data augmentation-assisted deep learning of hand-drawn partially colored sketches for visual search. PLoS One 21(3): e0344760. https://doi.org/10.1371/journal.pone.0344760

Published: March 12, 2026

Copyright: © 2026 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.