  The standard practice in Generative Adversarial Networks (GANs) discards the
discriminator during sampling. However, this sampling method loses valuable
information learned by the discriminator regarding the data distribution. In
this work, we propose a collaborative sampling scheme between the generator and
the discriminator for improved data generation. Guided by the discriminator,
our approach refines the generated samples through gradient-based updates at a
particular layer of the generator, shifting the generator distribution closer
to the real data distribution. Additionally, we present a practical
discriminator shaping method that can smoothen the loss landscape provided by
the discriminator for effective sample refinement. Through extensive
experiments on synthetic and image datasets, we demonstrate that our proposed
method can improve generated samples both quantitatively and qualitatively,
offering a new degree of freedom in GAN sampling.
