  Training Generative adversarial networks (GANs) stably is a challenging task.
The generator in GANs transform noise vectors, typically Gaussian distributed,
into realistic data such as images. In this paper, we propose a novel approach
for training GANs with images as inputs, but without enforcing any pairwise
constraints. The intuition is that images are more structured than noise, which
the generator can leverage to learn a more robust transformation. The process
can be made efficient by identifying closely related datasets, or a ``friendly
neighborhood'' of the target distribution, inspiring the moniker, Spider GAN.
To define friendly neighborhoods leveraging proximity between datasets, we
propose a new measure called the signed inception distance (SID), inspired by
the polyharmonic kernel. We show that the Spider GAN formulation results in
faster convergence, as the generator can discover correspondence even between
seemingly unrelated datasets, for instance, between Tiny-ImageNet and CelebA
faces. Further, we demonstrate cascading Spider GAN, where the output
distribution from a pre-trained GAN generator is used as the input to the
subsequent network. Effectively, transporting one distribution to another in a
cascaded fashion until the target is learnt -- a new flavor of transfer
learning. We demonstrate the efficacy of the Spider approach on DCGAN,
conditional GAN, PGGAN, StyleGAN2 and StyleGAN3. The proposed approach achieves
state-of-the-art Frechet inception distance (FID) values, with one-fifth of the
training iterations, in comparison to their baseline counterparts on
high-resolution small datasets such as MetFaces, Ukiyo-E Faces and AFHQ-Cats.
