AntoineH/smart_augmentation: 'Smart_aug' objective is to make data augmentation differentiable, thus allowing to learn its parameters, with 'Higher' objects, such as 'Data_aug' classes. The meta-learning of the data augmentation parameter is performed jointly with the training of the model. Thus it minimize the overhead compared to other data augmentation learning techniques.

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'Smart_aug' objective is to make data augmentation differentiable, thus allowing to learn its parameters, with 'Higher' objects, such as 'Data_aug' classes. The meta-learning of the data augmentation parameter is performed jointly with the training of the model. Thus it minimize the overhead compared to other data augmentation learning techniques.

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Harle, Antoine (Contracteur) 0e7ec8b5b0 Suite de test brutus		2019-11-20 16:06:27 -05:00
FAR-HO	Initial Commit	2019-11-08 11:28:06 -05:00
Gradient-Descent-The-Ultimate-Optimizer	Initial Commit	2019-11-08 11:28:06 -05:00
higher	Suite de test brutus	2019-11-20 16:06:27 -05:00
PBA	Suite de test brutus	2019-11-20 16:06:27 -05:00
.gitignore	Ignore sample dir	2019-11-11 17:03:31 -05:00