# Noise Robust Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1911.11776 · HTML: https://mlsanity.com/paper/1911.11776/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2019.

## Built on by (most cited first, 17 of 17)

- [Regularizing Generative Adversarial Networks under Limited Data](https://mlsanity.com/paper/2104.03310/) (2021)
- [Learning to Drop Points for LiDAR Scan Synthesis](https://mlsanity.com/paper/2102.11952/) (2021)
- [Optimal Transport for Unsupervised Denoising Learning](https://mlsanity.com/paper/2108.02574/) (2021)
- [GlowGAN: Unsupervised Learning of HDR Images from LDR Images in the Wild](https://mlsanity.com/paper/2211.12352/) (2022)
- [Generative Range Imaging for Learning Scene Priors of 3D LiDAR Data](https://mlsanity.com/paper/2210.11750/) (2022)
- [AR-NeRF: Unsupervised Learning of Depth and Defocus Effects from Natural Images with Aperture Rendering Neural Radiance Fields](https://mlsanity.com/paper/2206.06100/) (2022)
- [Influence Estimation for Generative Adversarial Networks](https://mlsanity.com/paper/2101.08367/) (2021)
- [Unsupervised Learning of Depth and Depth-of-Field Effect from Natural Images with Aperture Rendering Generative Adversarial Networks](https://mlsanity.com/paper/2106.13041/) (2021)
- [Robust Vector Quantized-Variational Autoencoder](https://mlsanity.com/paper/2202.01987/) (2022)
- [Extreme Image Transformations Affect Humans and Machines Differently](https://mlsanity.com/paper/2212.13967/) (2022)
- [SCoRe: Clean Image Generation from Diffusion Models Trained on Noisy Images](https://mlsanity.com/paper/2604.09436/) (2026)
- [Unsupervised Monocular Depth Estimation Based on Hierarchical Feature-Guided Diffusion](https://mlsanity.com/paper/2406.09782/) (2024)
- [BrainCodec: Neural fMRI codec for the decoding of cognitive brain states](https://mlsanity.com/paper/2410.04383/) (2024)
- [DGL-GAN: Discriminator Guided Learning for GAN Compression](https://mlsanity.com/paper/2112.06502/) (2021)
- [FORESEE: Multimodal and Multi-view Representation Learning for Robust Prediction of Cancer Survival](https://mlsanity.com/paper/2405.07702/) (2024)
- [Image Restoration Learning via Noisy Supervision in the Fourier Domain](https://mlsanity.com/paper/2506.00564/) (2025)
- [Noise-Robust Conditional Flow Matching: Generating Clean Samples from Noisy Datasets](https://mlsanity.com/paper/2608.00064/) (2026)

## Abstract (arXiv)

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/.