Disentangle to create: Architecture-level disentangled diffusion model for few-shot Chinese font generation
- DOI
- 10.1016/j.neunet.2026.109825
- Published
- 2027-02
- Container
- Neural Networks
- Publisher
- Elsevier BV
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2026.109825,
title = {Disentangle to create: Architecture-level disentangled diffusion model for few-shot Chinese font generation},
author = {Jie Li and Suorong Yang and Jian Zhao and Furao Shen},
year = {2027},
journal = {Neural Networks},
doi = {10.1016/j.neunet.2026.109825},
url = {https://doi.org/10.1016/j.neunet.2026.109825}
}RIS
TY - JOUR TI - Disentangle to create: Architecture-level disentangled diffusion model for few-shot Chinese font generation AU - Jie Li AU - Suorong Yang AU - Jian Zhao AU - Furao Shen PY - 2027 JO - Neural Networks DO - 10.1016/j.neunet.2026.109825 UR - https://doi.org/10.1016/j.neunet.2026.109825 ER -
APA
Li, J., Yang, S., Zhao, J., & Shen, F. (2027). Disentangle to create: Architecture-level disentangled diffusion model for few-shot Chinese font generation. Neural Networks. https://doi.org/10.1016/j.neunet.2026.109825
Source records
- crossref · retrieved 2026-11-09T23:59:01.915Z