Found 471 results, showing the newest relevant preprints. Sort by relevancy only.Update me on new preprints

Comparison of Generative Adversarial Networks Architectures Which Reduce Mode Collapse

Generative Adversarial Networks are known for their high quality outputs and versatility. Expand abstract.
4 days ago
10/10 relevant
arXiv

Prescribed Generative Adversarial Networks

Generative adversarial networks (GANs) are a powerful approach to unsupervised learning. Expand abstract.
5 days ago
10/10 relevant
arXiv

Unconstrained Road Marking Recognition with Generative Adversarial Networks

With the following two major contributions: 1) The proposed deblurring network can successfully recover a clean road marking from a blurred one by adopting generative adversarial networks (GAN). 2) The proposed data augmentation method, based on mutual information, can preserve and learn semantic context from the given... Expand abstract.
5 days ago
10/10 relevant
arXiv

Semantic Preserving Generative Adversarial Models

We introduce generative adversarial models in which the discriminator is replaced by a calibrated (non-differentiable) classifier repeatedly enhanced by domain relevant features. Expand abstract.
7 days ago
10/10 relevant
arXiv

Parallelizing Training of Deep Generative Models on Massive Scientific Datasets

We present a novel tournament method to train traditional as well as generative adversarial networks built on LBANN, a scalable deep learning framework optimized for HPC systems. Expand abstract.
9 days ago
4/10 relevant
arXiv

Unrestricted Adversarial Attacks for Semantic Segmentation

We demonstrate a simple yet effective method to generate unrestricted adversarial examples using conditional generative adversarial networks (CGAN) without any hand-crafted metric. Expand abstract.
9 days ago
4/10 relevant
arXiv

PPGAN: Privacy-preserving Generative Adversarial Network

To address this issue, we propose a Privacy-preserving Generative Adversarial Network (PPGAN) model, in which we achieve differential privacy in GANs by adding well-designed noise to the gradient during the model learning procedure. Expand abstract.
10 days ago
10/10 relevant
arXiv

On the estimation of the Wasserstein distance in generative models

One recent improvement in GAN literature is to use the Wasserstein distance as loss function leading to Wasserstein Generative Adversarial Networks (WGANs). Expand abstract.
12 days ago
4/10 relevant
arXiv

Improvement of Multiparametric MR Image Segmentation by Augmenting the Data with Generative Adversarial Networks for Glioma Patients

This study investigates the use of varying amounts of synthetic brain T1-weighted (T1), post-contrast T1-weighted (T1Gd), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR) MR images created by a generative adversarial network to overcome the lack of annotated medical image data in training separate... Expand abstract.
13 days ago
10/10 relevant
arXiv