  We propose a new type of General Adversarial Network (GAN) to resolve a
common issue with Deep Learning. We develop a novel architecture that can be
applied to existing latent vector based GAN structures that allows them to
generate on-the-fly images of any size. Existing GAN for image generation
requires uniform images of matching dimensions. However, publicly available
datasets, such as ImageNet contain thousands of different sizes. Resizing image
causes deformations and changing the image data, whereas as our network does
not require this preprocessing step. We make significant changes to the
standard data loading techniques to enable any size image to be loaded for
training. We also modify the network in two ways, by adding multiple inputs and
a novel dynamic resizing layer. Finally we make adjustments to the
discriminator to work on multiple resolutions. These changes can allow multiple
resolution datasets to be trained on without any resizing, if memory allows. We
validate our results on the ISIC 2019 skin lesion dataset. We demonstrate our
method can successfully generate realistic images at different sizes without
issue, preserving and understanding spatial relationships, while maintaining
feature relationships. We will release the source codes upon paper acceptance.
