Epileptic Seizure Forecasting With Generative Adversarial Networks
نویسندگان
چکیده
منابع مشابه
Automatic Colorization of Grayscale Images Using Generative Adversarial Networks
Automatic colorization of gray scale images poses a unique challenge in Information Retrieval. The goal of this field is to colorize images which have lost some color channels (such as the RGB channels or the AB channels in the LAB color space) while only having the brightness channel available, which is usually the case in a vast array of old photos and portraits. Having the ability to coloriz...
متن کاملConstruction with Generative Adversarial Networks
Three-dimensional (3D) Reconstruction is a vital and challenging research topic in advanced computer graphics and computer vision due to the intrinsic complexity and computation cost. Existing methods often produce holes, distortions and obscure parts in the reconstructed 3D models which are not adequate for real usage. The focus of this paper is to achieve high quality 3D reconstruction perfor...
متن کاملEvolutionary Generative Adversarial Networks
Generative adversarial networks (GAN) have been effective for learning generative models for real-world data. However, existing GANs (GAN and its variants) tend to suffer from training problems such as instability and mode collapse. In this paper, we propose a novel GAN framework called evolutionary generative adversarial networks (E-GAN) for stable GAN training and improved generative performa...
متن کاملUnrolled Generative Adversarial Networks
We introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator. This allows training to be adjusted between using the optimal discriminator in the generator’s objective, which is ideal but infeasible in practice, and using the current value of the discriminator, which is often unstable a...
متن کاملAnnealed Generative Adversarial Networks
Generative Adversarial Networks (GANs) have recently emerged as powerful generative models. GANs are trained by an adversarial process between a generative network and a discriminative network. It is theoretically guaranteed that, in the nonparametric regime, by arriving at the unique saddle point of a minimax objective function, the generative network generates samples from the data distributi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2944691