aug_mixup — Mixup augmentation¶
Group: Augmentation · Binding: n4m.sklearn.MixupAugmenter · C ABI: n4m_aug_mixup_*
Description¶
Batch-wise mixup augmentation.
Parameters¶
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Explanations¶
Bibliographic source¶
Zhang, H., Cisse, M., Dauphin, Y. N. & Lopez-Paz, D. (2018). mixup: Beyond Empirical Risk Minimization. ICLR 2018.
Mathematical principle¶
Forms convex combinations of sample pairs, \(\tilde{\mathbf{x}} = \lambda\mathbf{x}_i + (1-\lambda)\mathbf{x}_j\) and \(\tilde{y} = \lambda y_i + (1-\lambda) y_j\) with \(\lambda \sim \mathrm{Beta}(\alpha,\alpha)\), encouraging linear behaviour between training examples and regularising the calibration model.
Implementation¶
C ABI n4m_aug_mixup_* in libn4m (create / apply / destroy lifecycle), wrapped by n4m.sklearn.MixupAugmenter. The same numerical kernel backs every language binding.
Usage¶
from n4m.sklearn import MixupAugmenter
op = MixupAugmenter()
X_transformed = op.fit_transform(X)
See also: methods index · interactive dashboard