An Evaluation of Zero-Cost Proxies - From Neural Architecture Performance 
Prediction to Model Robustness

Lukasik, Jovita; Moeller, Michael; Keuper, Margret

doi:10.1007/978-3-031-54605-1_40

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

An Evaluation of Zero-Cost Proxies - From Neural Architecture Performance Prediction to Model Robustness

MPS-Authors

/persons/resource/persons180612

Keuper, Margret
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

External Resource

https://rdcu.be/dErn5
(Publisher version)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Lukasik, J., Moeller, M., & Keuper, M. (2023). An Evaluation of Zero-Cost Proxies - From Neural Architecture Performance Prediction to Model Robustness. In U. Köthe, & C. Rother (Eds.), Pattern Recognition (pp. 624-638). Berlin: Springer. doi:10.1007/978-3-031-54605-1_40.

Cite as: https://hdl.handle.net/21.11116/0000-000F-243C-6

Abstract

There is no abstract available