… Figure 6: Various image augmentations have different effect on the agent’s performance.
Overall, we conclude that using image augmentations helps to fight overfitting. Moreover, we …

… Figure 6: Various image augmentations have different effect on the agent’s performance.
Overall, we conclude that using image augmentations helps to fight overfitting. Moreover, we …

… image augmentation scheme using deep reinforcement learning (DRL) to improve the
performance of a deep … in the performance of single image augmentation methods. It introduces a …

… day images to night images or gray images to color images, … task where input images are
transformed to retouched images. In … ber of augmented pairs, in which a reference image is asso…

… Many image augmentation algorithms have been … of image augmentation for deep learning
using a novel informative taxonomy. To examine the basic objective of image augmentation, …

… In addition, to alleviate the problem of insufficient augmentation of some images, we …
adequacy of image augmentation by the change of the augmentation trajectory of each image. Then…

… joint-training deep reinforcement learning framework for image augmentation. A segmentation
… In this way, the segmentation network learns to mask unimportant imaging features. Our …

… imaging, which are roughly divided into three main categories: (i) parametric medical image
… including hyperparameter tuning, selecting augmentation strategies, and neural architecture …

… reconstruction, in particular to enable low dose imaging … image data which are needed to
train these neural networks. We propose to overcome this bottleneck via a deep reinforcement …

… The application of augmentation methods based on … to augmentation techniques, this paper
will briefly discuss other characteristics of Data Augmentation such as test-time augmentation…