  Score-based generative models (SGMs) show the state-of-the-art sampling
quality and diversity. However, their training/sampling complexity is
notoriously high due to the highly complicated forward/reverse processes, so
they are not suitable for resource-limited settings. To solving this problem,
learning a simpler process is gathering much attention currently. We present an
enhanced GAN-based denoising method, called SPI-GAN, using our proposed
straight-path interpolation definition. To this end, we propose a GAN
architecture i) denoising through the straight-path and ii) characterized by a
continuous mapping neural network for imitating the denoising path. This
approach drastically reduces the sampling time while achieving as high sampling
quality and diversity as SGMs. As a result, SPI-GAN is one of the best-balanced
models among the sampling quality, diversity, and time for CIFAR-10, and
CelebA-HQ-256.
