  This paper presents a new method for shadow removal using unpaired data,
enabling us to avoid tedious annotations and obtain more diverse training
samples. However, directly employing adversarial learning and cycle-consistency
constraints is insufficient to learn the underlying relationship between the
shadow and shadow-free domains, since the mapping between shadow and
shadow-free images is not simply one-to-one. To address the problem, we
formulate Mask-ShadowGAN, a new deep framework that automatically learns to
produce a shadow mask from the input shadow image and then takes the mask to
guide the shadow generation via re-formulated cycle-consistency constraints.
Particularly, the framework simultaneously learns to produce shadow masks and
learns to remove shadows, to maximize the overall performance. Also, we
prepared an unpaired dataset for shadow removal and demonstrated the
effectiveness of Mask-ShadowGAN on various experiments, even it was trained on
unpaired data.
