Towards Trustworthy Dataset Distillation: A Benchmark of Privacy, Fairness and Robustness
Author:
Affiliation:
1. Fraunhofer FOKUS,Berlin,Germany
2. Mohamed Bin Zayed University of Artificial Intelligence,Abu Dhabi,United Arab Emirates
3. Technical University of Munich,Munich,Germany
Funder
Ministry of Education
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10649807/10649898/10650522.pdf?arnumber=10650522
Reference42 articles.
1. Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines
2. Dataset distillation;Wang,2018
3. Practical coreset constructions for machine learning;Bachem,2017
4. Dataset Distillation by Matching Training Trajectories
5. Privacy for free: How does dataset condensation help privacy?;Dong,2022
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