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Why Mixup Improves the Model Performance
v1v2v3v4 (latest)

Why Mixup Improves the Model Performance

International Conference on Artificial Neural Networks (ICANN), 2020
11 June 2020
Masanari Kimura
ArXiv (abs)PDFHTML

Papers citing "Why Mixup Improves the Model Performance"

6 / 6 papers shown
Test-Time Augmentation Meets Variational Bayes
Test-Time Augmentation Meets Variational Bayes
Masanari Kimura
Howard Bondell
OODBDLTDI
271
2
0
19 Sep 2024
A Short Survey on Importance Weighting for Machine Learning
A Short Survey on Importance Weighting for Machine Learning
Masanari Kimura
H. Hino
251
11
0
15 Mar 2024
Understanding Test-Time Augmentation
Understanding Test-Time Augmentation
Masanari Kimura
ViT
158
49
0
10 Feb 2024
Selective Mixup Helps with Distribution Shifts, But Not (Only) because
  of Mixup
Selective Mixup Helps with Distribution Shifts, But Not (Only) because of MixupInternational Conference on Machine Learning (ICML), 2023
Damien Teney
Yongfeng Zhang
Ehsan Abbasnejad
384
9
0
26 May 2023
Generalization Bounds for Set-to-Set Matching with Negative Sampling
Generalization Bounds for Set-to-Set Matching with Negative SamplingInternational Conference on Neural Information Processing (ICONIP), 2023
Masanari Kimura
160
3
0
25 Feb 2023
Learning PAC-Bayes Priors for Probabilistic Neural Networks
Learning PAC-Bayes Priors for Probabilistic Neural Networks
Maria Perez-Ortiz
Omar Rivasplata
Benjamin Guedj
M. Gleeson
Jingyu Zhang
John Shawe-Taylor
M. Bober
J. Kittler
UQCV
233
33
0
21 Sep 2021
1