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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

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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

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Additional Info

Field	Value
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