  We devise a novel neural network-based universal denoiser for the
finite-input, general-output (FIGO) channel. Based on the assumption of known
noisy channel densities, which is realistic in many practical scenarios, we
train the network such that it can denoise as well as the best sliding window
denoiser for any given underlying clean source data. Our algorithm, dubbed as
Generalized CUDE (Gen-CUDE), enjoys several desirable properties; it can be
trained in an unsupervised manner (solely based on the noisy observation data),
has much smaller computational complexity compared to the previously developed
universal denoiser for the same setting, and has much tighter upper bound on
the denoising performance, which is obtained by a theoretical analysis. In our
experiments, we show such tighter upper bound is also realized in practice by
showing that Gen-CUDE achieves much better denoising results compared to other
strong baselines for both synthetic and real underlying clean sequences.
