  Recently, there is a vast interest in developing image feature learning
methods that are independent of the training data, such as deep image prior,
InGAN, SinGAN, and DCIL. These methods are unsupervised and are used to perform
low-level vision tasks such as image restoration, image editing, and image
synthesis. In this work, we proposed a new training data-independent framework,
called Deep Contextual Features Learning (DeepCFL), to perform image synthesis
and image restoration based on the semantics of the input image. The contextual
features are simply the high dimensional vectors representing the semantics of
the given image. DeepCFL is a single image GAN framework that learns the
distribution of the context vectors from the input image. We show the
performance of contextual learning in various challenging scenarios:
outpainting, inpainting, and restoration of randomly removed pixels. DeepCFL is
applicable when the input source image and the generated target image are not
aligned. We illustrate image synthesis using DeepCFL for the task of image
resizing.
