  Non-uniform and multi-illuminant color constancy are important tasks, the
solution of which will allow to discard information about lighting conditions
in the image. Non-uniform illumination and shadows distort colors of real-world
objects and mostly do not contain valuable information. Thus, many computer
vision and image processing techniques would benefit from automatic discarding
of this information at the pre-processing step. In this work we propose novel
view on this classical problem via generative end-to-end algorithm based on
image conditioned Generative Adversarial Network. We also demonstrate the
potential of the given approach for joint shadow detection and removal. Forced
by the lack of training data, we render the largest existing shadow removal
dataset and make it publicly available. It consists of approximately 6,000
pairs of wide field of view synthetic images with and without shadows.
