  Aiming to produce sufficient and diverse training samples, data augmentation
has been demonstrated for its effectiveness in training deep models. Regarding
that the criterion of the best augmentation is challenging to define, we in
this paper present a novel learning-based augmentation method termed as
DeepAugNet, which formulates the final augmented data as a collection of
several sequentially augmented subsets. Specifically, the current augmented
subset is required to maximize the performance improvement compared with the
last augmented subset by learning the deterministic augmentation policy using
deep reinforcement learning. By introducing an unified optimization goal,
DeepAugNet intends to combine the data augmentation and the deep model training
in an end-to-end training manner which is realized by simultaneously training a
hybrid architecture of dueling deep Q-learning algorithm and a surrogate deep
model. We extensively evaluated our proposed DeepAugNet on various benchmark
datasets including Fashion MNIST, CUB, CIFAR-100 and WebCaricature. Compared
with the current state-of-the-arts, our method can achieve a significant
improvement in small-scale datasets, and a comparable performance in
large-scale datasets. Code will be available soon.
