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PUMA: margin-based data pruning

PUMA: margin-based data pruning

10 May 2024
Javier Maroto
Pascal Frossard
    AAML
ArXivPDFHTML

Papers citing "PUMA: margin-based data pruning"

5 / 5 papers shown
Title
Not All Samples Should Be Utilized Equally: Towards Understanding and
  Improving Dataset Distillation
Not All Samples Should Be Utilized Equally: Towards Understanding and Improving Dataset Distillation
Shaobo Wang
Yantai Yang
Qilong Wang
Kaixin Li
Linfeng Zhang
Junchi Yan
DD
41
4
0
22 Aug 2024
On the Convergence and Robustness of Adversarial Training
On the Convergence and Robustness of Adversarial Training
Yisen Wang
Xingjun Ma
James Bailey
Jinfeng Yi
Bowen Zhou
Quanquan Gu
AAML
192
345
0
15 Dec 2021
ImageNet-21K Pretraining for the Masses
ImageNet-21K Pretraining for the Masses
T. Ridnik
Emanuel Ben-Baruch
Asaf Noy
Lihi Zelnik-Manor
SSeg
VLM
CLIP
166
684
0
22 Apr 2021
Instance adaptive adversarial training: Improved accuracy tradeoffs in
  neural nets
Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets
Yogesh Balaji
Tom Goldstein
Judy Hoffman
AAML
124
102
0
17 Oct 2019
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
250
5,830
0
08 Jul 2016
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