  Generative adversarial networks (GANs) are neural networks that learn data
distributions through adversarial training. In intensive studies, recent GANs
have shown promising results for reproducing training images. However, in spite
of noise, they reproduce images with fidelity. As an alternative, we propose a
novel family of GANs called noise robust GANs (NR-GANs), which can learn a
clean image generator even when training images are noisy. In particular,
NR-GANs can solve this problem without having complete noise information (e.g.,
the noise distribution type, noise amount, or signal-noise relationship). To
achieve this, we introduce a noise generator and train it along with a clean
image generator. However, without any constraints, there is no incentive to
generate an image and noise separately. Therefore, we propose distribution and
transformation constraints that encourage the noise generator to capture only
the noise-specific components. In particular, considering such constraints
under different assumptions, we devise two variants of NR-GANs for
signal-independent noise and three variants of NR-GANs for signal-dependent
noise. On three benchmark datasets, we demonstrate the effectiveness of NR-GANs
in noise robust image generation. Furthermore, we show the applicability of
NR-GANs in image denoising. Our code is available at
https://github.com/takuhirok/NR-GAN/.
