Interpretability-preserving knowledge distillation via multi-granular feature alignment for resource-efficient CNNs

Shiv Singh, Neelu Jyothi Ahuja

Open source

DOI
10.1007/s10791-026-10123-y
Published
2026-05-26
Container
Discover Computing
Publisher
Springer Science and Business Media LLC
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

Cite this work

BibTeX

@article{allodium:10.1007/s10791-026-10123-y,
  title = {Interpretability-preserving knowledge distillation via multi-granular feature alignment for resource-efficient CNNs},
  author = {Shiv Singh and Neelu Jyothi Ahuja},
  year = {2026},
  journal = {Discover Computing},
  doi = {10.1007/s10791-026-10123-y},
  url = {https://doi.org/10.1007/s10791-026-10123-y}
}

RIS

TY  - JOUR
TI  - Interpretability-preserving knowledge distillation via multi-granular feature alignment for resource-efficient CNNs
AU  - Shiv Singh
AU  - Neelu Jyothi Ahuja
PY  - 2026
JO  - Discover Computing
DO  - 10.1007/s10791-026-10123-y
UR  - https://doi.org/10.1007/s10791-026-10123-y
ER  - 

APA

Singh, S., & Ahuja, N. J. (2026). Interpretability-preserving knowledge distillation via multi-granular feature alignment for resource-efficient CNNs. Discover Computing. https://doi.org/10.1007/s10791-026-10123-y

Source records