MixupE: Understanding and Improving Mixup from Directional Derivative Perspective

Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels.

Data and Resources

Cite this as

Yingtian Zou, Vikas Verma, Sarthak Mittal, Wai Hoh Tang, Hieu Pham, Juho Kannala, Yoshua Bengio, Arno Solin, Kenji Kawaguchi (2024). Dataset: MixupE: Understanding and Improving Mixup from Directional Derivative Perspective. https://doi.org/10.57702/0dqqe85n

DOI retrieved: December 3, 2024

Additional Info

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Created December 3, 2024
Last update December 3, 2024
Defined In https://doi.org/10.48550/arXiv.2212.13381
Author Yingtian Zou
More Authors
Vikas Verma
Sarthak Mittal
Wai Hoh Tang
Hieu Pham
Juho Kannala
Yoshua Bengio
Arno Solin
Kenji Kawaguchi
Homepage https://github.com/oneHuster/MixupE