Survey: Image Mixing and Deleting for Data Augmentation
- URL: http://arxiv.org/abs/2106.07085v1
- Date: Sun, 13 Jun 2021 20:32:24 GMT
- Title: Survey: Image Mixing and Deleting for Data Augmentation
- Authors: Humza Naveed
- Abstract summary: Image mixing and deleting is a sub-area within data augmentation.
Model trained with this approach has shown to perform and generalize well.
Due to its low compute cost and success in recent past, many techniques of image mixing and deleting are proposed.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Data augmentation has been widely used to improve deep nerual networks
performance. Numerous approaches are suggested, for example, dropout,
regularization and image augmentation, to avoid over-ftting and enhancing
generalization of neural networks. One of the sub-area within data augmentation
is image mixing and deleting. This specific type of augmentation either mixes
two images or delete image regions to hide or make certain characteristics of
images confusing for the network to force it to emphasize on overall structure
of object in image. The model trained with this approach has shown to perform
and generalize well as compared to one trained without imgage mixing or
deleting. Additional benefit achieved with this method of training is
robustness against image corruptions. Due to its low compute cost and success
in recent past, many techniques of image mixing and deleting are proposed. This
paper provides detailed review on these devised approaches, dividing
augmentation strategies in three main categories cut and delete, cut and mix
and mixup. The second part of paper emprically evaluates these approaches for
image classification, finegrained image recognition and object detection where
it is shown that this category of data augmentation improves the overall
performance for deep neural networks.
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