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29 papers · ranked by Valyu relevance

01
AarXiv2018
Generative Adversarial Networks: An Overview
Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran + 2 more
'Biswa Sengupta' 'Anil A. Bharath'] Abstract—Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can…
97relevance
02
AarXiv31 May 2016
Adversarial Feature Learning
Jeff Donahue, Philipp Krähenbühl, Trevor Darrell
The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing that the latent space of such generators captures semantic variation in the…
97relevance
03
PPubMed4 Feb 2022
Stock Price Forecasting by a Deep Convolutional Generative Adversarial Network
Alessio Staffini
Stock market prices are known to be very volatile and noisy, and their accurate forecasting is a challenging problem. Traditionally, both linear and non-linear methods (such as ARIMA and LSTM) have been proposed and successfully applied to stock market prediction, but there is room to develop models that further reduce…
95relevance
04
PPubMed13 Apr 2022
A generative adversarial network for synthetization of regions of interest based on digital mammograms
Olaide N. Oyelade, Absalom E. Ezugwu, Mubarak S. Almutairi, Apu Kumar Saha + 2 more
'Apu Kumar Saha' 'Laith Abualigah' 'Haruna Chiroma'] Deep learning (DL) models are becoming pervasive and applicable to computer vision, image processing, and synthesis problems. The performance of these models is often improved through architectural configuration, tweaks, the use of enormous training data, and…
94relevance
05
PPubMed23 Nov 2021
Generative adversarial networks for generating synthetic features for Wi-Fi signal quality Generative adversarial networks for Wi-Fi signal quality
Mauro Castelli, Luca Manzoni, Tatiane Espindola, Aleš Popovič + 2 more
Wireless networks are among the fundamental technologies used to connect people. Considering the constant advancements in the field, telecommunication operators must guarantee a high-quality service to keep their customer portfolio. To ensure this high-quality service, it is common to establish partnerships with…
94relevance
06
PPubMed4 Apr 2020
Utilizing Amari-Alpha Divergence to Stabilize the Training of Generative Adversarial Networks
Likun Cai, Yanjie Chen, Ning Cai, Wei Cheng + 1 more
Generative Adversarial Nets (GANs) are one of the most popular architectures for image generation, which has achieved significant progress in generating high-resolution, diverse image samples. The normal GANs are supposed to minimize the Kullback-Leibler divergence between distributions of natural and generated images.…
92relevance
07
PPubMed20 Jun 2022
FDG-PET to T1 Weighted MRI Translation with 3D Elicit Generative Adversarial Network (E-GAN)
Farideh Bazangani, Frédéric J. P. Richard, Badih Ghattas, Eric Guedj + 3 more
Objective: With the strengths of deep learning, computer-aided diagnosis (CAD) is a hot topic for researchers in medical image analysis. One of the main requirements for training a deep learning model is providing enough data for the network. However, in medical images, due to the difficulties of data collection and…
92relevance
08
PPubMed29 Jul 2021
Conditional-GAN Based Data Augmentation for Deep Learning Task Classifier Improvement Using fNIRS Data Wickramaratne and Mahmud Deep Learning for fNIRS
Sajila D. Wickramaratne, Md.Shaad Mahmud
Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique used for mapping the functioning human cortex. fNIRS can be widely used in population studies due to the technology’s economic, non-invasive, and portable nature. fNIRS can be used for task classification, a crucial part of functioning with…
92relevance
09
PPubMed18 Oct 2023
Improving the quality of image generation in art with top- <i> k </i> training and cyclic generative methods
Laura Vela, Félix Fuentes-Hurtado, Adrián Colomer
The creation of artistic images through the use of Artificial Intelligence is an area that has been gaining interest in recent years. In particular, the ability of Neural Networks to separate and subsequently recombine the style of different images, generating a new artistic image with the desired style, has been a…
91relevance
10
PPubMed20 Apr 2021
Generative Adversarial Network Performance in Low-Dimensional Settings
Felix Jimenez, Amanda Koepke, Mary Gregg, Michael Frey
Settings Authors: ['Felix Jimenez' 'Amanda Koepke' 'Mary Gregg' 'Michael Frey'] A generative adversarial network (GAN) is an artifcial neural network with a distinctive training architecture, designed to create examples that faithfully reproduce a target distribution. GANs have recently had particular success in…
91relevance
11
AarXiv2021
Regularized Generative Adversarial Network
Gabriele Di Cerbo, Ali Hirsa, Ahmad Shayaan
We propose a framework for generating samples from a probability distribution that differs from the probability distribution of the training set. We use an adversarial process that simultaneously trains three networks, a generator and two discriminators. We refer to this new model as regularized generative adversarial…
90relevance
12
BbioRxiv6 Jul 2018
Generating Realistic Morphologies of Neurons in Rodent Hippocampus with DCGAN
Darian H. Hadjiabadi
Dendritic size and branching patterns are important features of neural form and function. However, current computational models of neuronal networks use simplistic cylindrical geometries to mimic dendritic arborizations. One reason for this is that current methods to generate dendritic trees have rigid a priori…
90relevance
13
AarXiv13 Jan 2019
Yes, we GAN: Applying adversarial techniques for autonomous driving
Michal Uřičář, Pavel Křížek, David Hurych, Ibrahim Sobh + 2 more
'Senthil Yogamani' 'Patrick Denny'] Generative Adversarial Networks (GAN) have gained a lot of popularity from their introduction in 2014 till present. Research on GAN is rapidly growing and there are many variants of the original GAN focusing on various aspects of deep learning. GAN are perceived as the most impactful…
90relevance
14
BbioRxiv4 Jan 2022
Quality assessment of anatomical MRI images from Generative Adversarial Networks: human assessment and image quality metrics
Matthias S. Treder, Ryan Codrai, Kamen A. Tsvetanov
Generative Adversarial Networks (GANs) can synthesize brain images from image or noise input. So far, the gold standard for assessing the quality of the generated images has been human expert ratings. However, due to limitations of human assessment in terms of cost, scalability, and the limited sensitivity of the human…
90relevance
15
BbioRxiv4 Sep 2017
ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network
Mario Valerio Giuffrida, Hanno Scharr, Sotirios A Tsaftaris
In recent years, there has been an increasing interest in image-based plant phenotyping, applying state-of-the-art machine learning approaches to tackle challenging problems, such as leaf segmentation (a multi-instance problem) and counting. Most of these algorithms need labelled data to learn a model for the task at…
90relevance
16
BbioRxiv12 Jun 2020
Synthetic observations from deep generative models and binary omics data with limited sample size
Jens Nußberger, Frederic Boesel, Stefan Lenz, Harald Binder + 1 more
Deep generative models can be trained to represent the joint distribution of data, such as measurements of single nucleotide polymorphisms (SNPs) from several individuals. Subsequently, synthetic observations are obtained by drawing from this distribution. This has been shown to be useful for several tasks, such as…
89relevance
17
AarXiv2022
Generative Adversarial Network Applications in Creating a Meta-Universe
Soheyla Amirian, Thiab R. Taha, Khaled Rasheed, Hamid R. Arabnia
Generative Adversarial Networks (GANs) are machine learning methods that are used in many important and novel applications. For example, in imaging science, GANs are effectively utilized in generating image datasets, photographs of human faces, image and video captioning, image-to-image translation, text-to-image…
89relevance
18
AarXiv2018
Comparative Study on Generative Adversarial Networks
Saifuddin Hitawala
In recent years, there have been tremendous advancements in the field of machine learning. These advancements have been made through both academic as well as industrial research. Lately, a fair amount of research has been dedicated to the usage of generative models in the field of computer vision and image…
89relevance
19
AarXiv2018
Comparing Generative Adversarial Network Techniques for Image Creation and Modification
Mathijs Pieters, Marco Wiering
Generative adversarial networks (GANs) have demonstrated to be successful at generating realistic real-world images. In this paper we compare various GAN techniques, both supervised and unsupervised. The effects on training stability of different objective functions are compared. We add an encoder to the network…
88relevance
20
BbioRxiv20 Apr 2018
Generative Adversarial Networks Conditioned on Brain Activity Reconstruct Seen Images
Ghislain St-Yves, Thomas Naselaris
We consider the inference problem of reconstructing a visual stimulus from brain activity measurements (e.g. fMRI) that encode this stimulus. Recovering a complete image is complicated by the fact that neural representations are noisy, high-dimensional, and contain incomplete information about image details. Thus…
85relevance
21
AarXiv2022
Cooperate or Compete: A New Perspective on Training of Generative Networks
Sobhan Babu, Ravindra Guravannavar
GANs have two competing modules: the generator module is trained to generate new examples, and the discriminator module is trained to discriminate real examples from generated examples. The training procedure of GAN is modeled as a finitely repeated simultaneous game. Each module tries to increase its performance at…
83relevance
22
CchemRxiv27 Jun 2023
MoFlowGAN: Combining adversarial and likelihood learning for targeted molecular generation
Nathan Mancheun Lui, Max D Li, Matthew Ford
Deep generative models for molecular graphs offer a new avenue for property optimization in drug discovery. Optimizing differentiable models that generate molecular graphs is certainly faster, cheaper, and much more accessible than traditional methods of chemical synthesis. Recent advances in generative modeling have…
82relevance
23
BbioRxiv18 Jan 2021
Sampling from Disentangled Representations of Single-Cell Data Using Generative Adversarial Networks
Hengshi Yu, Joshua D. Welch
Deep generative models, including variational autoencoders (VAEs) and generative adversarial networks (GANs), have achieved remarkable successes in generating and manipulating highdimensional images. VAEs excel at learning disentangled image representations, while GANs excel at generating realistic images. Here, we…
82relevance
24
BbioRxiv2 Jul 2020
Hyperrealistic neural decoding: Reconstruction of face stimuli from fMRI measurements via the GAN latent space
Thirza Dado, Yağmur Güçlütürk, Luca Ambrogioni, Gabriëlle Ras + 3 more
We introduce a new framework for hyperrealistic reconstruction of perceived naturalistic stimuli from brain recordings. To this end, we embrace the use of generative adversarial networks (GANs) at the earliest step of our neural decoding pipeline by acquiring functional magnetic resonance imaging data as subjects…
81relevance
25
CchemRxiv22 Feb 2023
Data-Driven Deep Generative Design of Stable Spintronic Materials
Edirisuriya Siriwardane, Yong Zhao, Jianjun Hu
Discovering novel magnetic materials is essential for advancing the spintronic technology with significant applications in data communication, data storage, quantum computing, and etc. While Density functional theory (DFT) has been widely used for designing materials, its high computational demand for estimating the…
80relevance
26
CchemRxiv1 Jan 1970
Exploring the GDB-13 Chemical Space Using Deep Generative Models
Josep Arús-Pous, Thomas Blaschke, Silas Ulander, Jean-Louis Reymond + 2 more
Recent applications of Recurrent Neural Networks enable training models that sample the chemical space. In this study we train RNN with molecular string representations (SMILES) with a subset of the enumerated database GDB-13 (975 million molecules). We show that a model trained with 1 million structures (0.1 % of the…
68relevance
27
CchemRxiv9 Oct 2018
Exploring the GDB-13 Chemical Space Using Deep Generative Models
Josep Arús-Pous, Thomas Blaschke, Jean-Louis Reymond, Hongming Chen + 1 more
Recent applications of Recurrent Neural Networks enable training models that sample the chemical space. In this study we train RNN with molecular string representations (SMILES) with a subset of the enumerated database GDB-13 (975 million molecules). We show that a model trained with 1 million structures (0.1 % of the…
68relevance
28
CchemRxiv30 Apr 2024
Diff-Shape: A Novel Constrained Diffusion Model for Shape based De Novo Drug Design
Jie Lin, Mingyuan Xu, Hongming Chen
Shape-based virtual screening is a widely utilized method in ligand-based de novo drug design, aiming to identify molecules in chemical libraries that share similar 3D shapes but simultaneously possess novel 2D chemical structures compared to the reference compound. As an emerging technology, generative model is an…
68relevance
29
CchemRxiv18 Jul 2023
Inverse Design of Porous Materials: A Diffusion Model Approach
Junkil Park, Aseem Partap Singh Gill, Seyed Mohamad Moosavi, JIHAN KIM
The success of diffusion models in the field of image processing has propelled the creation of software such as Dall-E, Midjourney and Stable Diffusion, which are tools used for text-to-image generations. Mapping this workflow onto materials discovery, a new diffusion model was developed for the generation of pure…
63relevance

Results for generative adversarial network

Generative Adversarial Networks: An Overview

Adversarial Feature Learning

Stock Price Forecasting by a Deep Convolutional Generative Adversarial Network

A generative adversarial network for synthetization of regions of interest based on digital mammograms

Generative adversarial networks for generating synthetic features for Wi-Fi signal quality Generative adversarial networks for Wi-Fi signal quality

Utilizing Amari-Alpha Divergence to Stabilize the Training of Generative Adversarial Networks

FDG-PET to T1 Weighted MRI Translation with 3D Elicit Generative Adversarial Network (E-GAN)

Conditional-GAN Based Data Augmentation for Deep Learning Task Classifier Improvement Using fNIRS Data Wickramaratne and Mahmud Deep Learning for fNIRS

Improving the quality of image generation in art with top- <i> k </i> training and cyclic generative methods

Generative Adversarial Network Performance in Low-Dimensional Settings

Regularized Generative Adversarial Network

Generating Realistic Morphologies of Neurons in Rodent Hippocampus with DCGAN

Yes, we GAN: Applying adversarial techniques for autonomous driving

Quality assessment of anatomical MRI images from Generative Adversarial Networks: human assessment and image quality metrics

ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network

Synthetic observations from deep generative models and binary omics data with limited sample size

Generative Adversarial Network Applications in Creating a Meta-Universe

Comparative Study on Generative Adversarial Networks

Comparing Generative Adversarial Network Techniques for Image Creation and Modification

Generative Adversarial Networks Conditioned on Brain Activity Reconstruct Seen Images

Cooperate or Compete: A New Perspective on Training of Generative Networks

MoFlowGAN: Combining adversarial and likelihood learning for targeted molecular generation

Sampling from Disentangled Representations of Single-Cell Data Using Generative Adversarial Networks

Hyperrealistic neural decoding: Reconstruction of face stimuli from fMRI measurements via the GAN latent space

Data-Driven Deep Generative Design of Stable Spintronic Materials

Exploring the GDB-13 Chemical Space Using Deep Generative Models

Exploring the GDB-13 Chemical Space Using Deep Generative Models

Diff-Shape: A Novel Constrained Diffusion Model for Shape based De Novo Drug Design

Inverse Design of Porous Materials: A Diffusion Model Approach

Generative Adversarial Networks: An Overview

Adversarial Feature Learning

Stock Price Forecasting by a Deep Convolutional Generative Adversarial Network

A generative adversarial network for synthetization of regions of interest based on digital mammograms

Generative adversarial networks for generating synthetic features for Wi-Fi signal quality Generative adversarial networks for Wi-Fi signal quality

Utilizing Amari-Alpha Divergence to Stabilize the Training of Generative Adversarial Networks

FDG-PET to T1 Weighted MRI Translation with 3D Elicit Generative Adversarial Network (E-GAN)

Conditional-GAN Based Data Augmentation for Deep Learning Task Classifier Improvement Using fNIRS Data Wickramaratne and Mahmud Deep Learning for fNIRS

Improving the quality of image generation in art with top- <i> k </i> training and cyclic generative methods

Generative Adversarial Network Performance in Low-Dimensional Settings

Regularized Generative Adversarial Network

Generating Realistic Morphologies of Neurons in Rodent Hippocampus with DCGAN

Yes, we GAN: Applying adversarial techniques for autonomous driving

Quality assessment of anatomical MRI images from Generative Adversarial Networks: human assessment and image quality metrics

ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network

Synthetic observations from deep generative models and binary omics data with limited sample size

Generative Adversarial Network Applications in Creating a Meta-Universe

Comparative Study on Generative Adversarial Networks

Comparing Generative Adversarial Network Techniques for Image Creation and Modification

Generative Adversarial Networks Conditioned on Brain Activity Reconstruct Seen Images

Cooperate or Compete: A New Perspective on Training of Generative Networks

MoFlowGAN: Combining adversarial and likelihood learning for targeted molecular generation

Sampling from Disentangled Representations of Single-Cell Data Using Generative Adversarial Networks

Hyperrealistic neural decoding: Reconstruction of face stimuli from fMRI measurements via the GAN latent space

Data-Driven Deep Generative Design of Stable Spintronic Materials

Exploring the GDB-13 Chemical Space Using Deep Generative Models

Exploring the GDB-13 Chemical Space Using Deep Generative Models

Diff-Shape: A Novel Constrained Diffusion Model for Shape based De Novo Drug Design

Inverse Design of Porous Materials: A Diffusion Model Approach