Functional network connectivity (FNC)-based generative adversarial network (GAN) and its applications in classification of mental disorders
نویسندگان
چکیده
منابع مشابه
Energy-based Generative Adversarial Network
We introduce the “Energy-based Generative Adversarial Network” model (EBGAN) which views the discriminator as an energy function that associates low energies with the regions near the data manifold and higher energies with other regions. Similar to the probabilistic GANs, a generator is trained to produce contrastive samples with minimal energies, while the discriminator is trained to assign hi...
متن کاملTAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network
In this work, we present the Text Conditioned Auxiliary Classifier Generative Adversarial Network, (TAC-GAN) a text to image Generative Adversarial Network (GAN) for synthesizing images from their text descriptions. Former approaches have tried to condition the generative process on the textual data; but allying it to the usage of class information, known to diversify the generated samples and ...
متن کاملWasserstein Generative Adversarial Network
Recent advances in deep generative models give us new perspective on modeling highdimensional, nonlinear data distributions. Especially the GAN training can successfully produce sharp, realistic images. However, GAN sidesteps the use of traditional maximum likelihood learning and instead adopts an two-player game approach. This new training behaves very differently compared to ML learning. Ther...
متن کاملControllable Generative Adversarial Network
Although it is recently introduced, in last few years, generative adversarial network (GAN) has been shown many promising results to generate realistic samples. However, it is hardly able to control generated samples since input variables for a generator are from a random distribution. Some attempts have been made to control generated samples from GAN, but they have shown moderate results. Furt...
متن کاملAdversarial Examples Generation and Defense Based on Generative Adversarial Network
We propose a novel generative adversarial network to generate and defend adversarial examples for deep neural networks (DNN). The adversarial stability of a network D is improved by training alternatively with an additional network G. Our experiment is carried out on MNIST, and the adversarial examples are generated in an efficient way compared with wildly-used gradient based methods. After tra...
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: Journal of Neuroscience Methods
سال: 2020
ISSN: 0165-0270
DOI: 10.1016/j.jneumeth.2020.108756