  Image augmentation techniques apply transformation functions such as
rotation, shearing, or color distortion on an input image. These augmentations
were proven useful in improving neural networks' generalization ability. In
this paper, we present a novel augmentation operation, InAugment, that exploits
image internal statistics. The key idea is to copy patches from the image
itself, apply augmentation operations on them, and paste them back at random
positions on the same image. This method is simple and easy to implement and
can be incorporated with existing augmentation techniques. We test InAugment on
two popular datasets -- CIFAR and ImageNet. We show improvement over
state-of-the-art augmentation techniques. Incorporating InAugment with Auto
Augment yields a significant improvement over other augmentation techniques
(e.g., +1% improvement over multiple architectures trained on the CIFAR
dataset). We also demonstrate an increase for ResNet50 and EfficientNet-B3
top-1's accuracy on the ImageNet dataset compared to prior augmentation
methods. Finally, our experiments suggest that training convolutional neural
network using InAugment not only improves the model's accuracy and confidence
but its performance on out-of-distribution images.
