You can use it for data augmentation.
It’s used for example in the ImageNet training 1.1k.
The scale might seem a little odd, but have a look at the Inception paper Going deeper with convolutions 327, section 6:

Still, one prescription that was verified to work very well after the competition includes sampling of various sized patches of the image whose size is distributed evenly between 8% and 100% of the image area and whose aspect ratio is chosen randomly between 3/4 and 4/3.

So they used indeed this scaling to train their InceptionNet.