  In most existing learning systems, images are typically viewed as 2D pixel
arrays. However, in another paradigm gaining popularity, a 2D image is
represented as an implicit neural representation (INR) - an MLP that predicts
an RGB pixel value given its (x,y) coordinate. In this paper, we propose two
novel architectural techniques for building INR-based image decoders:
factorized multiplicative modulation and multi-scale INRs, and use them to
build a state-of-the-art continuous image GAN. Previous attempts to adapt INRs
for image generation were limited to MNIST-like datasets and do not scale to
complex real-world data. Our proposed INR-GAN architecture improves the
performance of continuous image generators by several times, greatly reducing
the gap between continuous image GANs and pixel-based ones. Apart from that, we
explore several exciting properties of the INR-based decoders, like
out-of-the-box superresolution, meaningful image-space interpolation,
accelerated inference of low-resolution images, an ability to extrapolate
outside of image boundaries, and strong geometric prior. The project page is
located at https://universome.github.io/inr-gan.
