Manifold Mixup: Learning Better Representations by Interpolating Hidden States
Deep networks often perform well on the data distribution on which they are trained, yet give incorrect (and often very confident) answers when evaluated on points from off of the training distribution. This is exemplified by the adversarial examples phenomenon but can also be seen in terms of model generalization and domain shift... (read more)
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Methods used in the Paper
| METHOD | TYPE | |
|---|---|---|
Manifold Mixup
|
Regularization | |
Mixup
|
Image Data Augmentation |