Combating Overfitting of Adversarial Training Efficiently via Balanced Instance Adaptive Defense.
- DOI
- 10.1109/tip.2026.3732307
- Published
- 2026
- Container
- IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tip.2026.3732307,
title = {Combating Overfitting of Adversarial Training Efficiently via Balanced Instance Adaptive Defense.},
author = {Zhang X and Wu S and Shan Q and Wang Y and Su J},
year = {2026},
journal = {IEEE transactions on image processing : a publication of the IEEE Signal Processing Society},
doi = {10.1109/tip.2026.3732307},
url = {https://doi.org/10.1109/tip.2026.3732307}
}RIS
TY - JOUR TI - Combating Overfitting of Adversarial Training Efficiently via Balanced Instance Adaptive Defense. AU - Zhang X AU - Wu S AU - Shan Q AU - Wang Y AU - Su J PY - 2026 JO - IEEE transactions on image processing : a publication of the IEEE Signal Processing Society DO - 10.1109/tip.2026.3732307 UR - https://doi.org/10.1109/tip.2026.3732307 ER -
APA
X, Z., S, W., Q, S., Y, W., & J, S. (2026). Combating Overfitting of Adversarial Training Efficiently via Balanced Instance Adaptive Defense.. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. https://doi.org/10.1109/tip.2026.3732307
Source records
- pubmed · retrieved 2026-09-25T08:03:38.962Z