  Generative adversarial networks (GANs) are widely used in image generation
tasks, yet the generated images are usually lack of texture details. In this
paper, we propose a general framework, called Progressively Unfreezing
Perceptual GAN (PUPGAN), which can generate images with fine texture details.
Particularly, we propose an adaptive perceptual discriminator with a
pre-trained perceptual feature extractor, which can efficiently measure the
discrepancy between multi-level features of the generated and real images. In
addition, we propose a progressively unfreezing scheme for the adaptive
perceptual discriminator, which ensures a smooth transfer process from a large
scale classification task to a specified image generation task. The qualitative
and quantitative experiments with comparison to the classical baselines on
three image generation tasks, i.e. single image super-resolution, paired
image-to-image translation and unpaired image-to-image translation demonstrate
the superiority of PUPGAN over the compared approaches.
