  We present a learned unsupervised denoising method for arbitrary types of
data, which we explore on images and one-dimensional signals. The training is
solely based on samples of noisy data and examples of noise, which --
critically -- do not need to come in pairs. We only need the assumption that
the noise is independent and additive (although we describe how this can be
extended). The method rests on a Wasserstein Generative Adversarial Network
setting, which utilizes two critics and one generator.
