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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root 61dad1ad78 Test brutus suite		2019-11-25 16:36:35 +00:00
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	Test brutus suite	2019-11-25 16:36:35 +00:00
PBA	Fix LeNet Tensorflow	2019-11-21 12:29:17 -05:00
.gitignore	Ignore sample dir	2019-11-11 17:03:31 -05:00