scholarly journals A Survey on Generative Adversarial Networks: Variants, Applications, and Training

2022 ◽  
Vol 54 (8) ◽  
pp. 1-49
Author(s):  
Abdul Jabbar ◽  
Xi Li ◽  
Bourahla Omar
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Nash Equilibrium ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Data Generation ◽  
Crucial Issue ◽  
Practical Applications ◽  
Adversarial Networks ◽  
And Training

The Generative Models have gained considerable attention in unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to their outstanding data generation capability. Many GAN models have been proposed, and several practical applications have emerged in various domains of computer vision and machine learning. Despite GANs excellent success, there are still obstacles to stable training. The problems are Nash equilibrium, internal covariate shift, mode collapse, vanishing gradient, and lack of proper evaluation metrics. Therefore, stable training is a crucial issue in different applications for the success of GANs. Herein, we survey several training solutions proposed by different researchers to stabilize GAN training. We discuss (I) the original GAN model and its modified versions, (II) a detailed analysis of various GAN applications in different domains, and (III) a detailed study about the various GAN training obstacles as well as training solutions. Finally, we reveal several issues as well as research outlines to the topic.

scholarly journals Human Face Generation using Deep Convolution Generative Adversarial Network

2021 ◽  
Vol 7 (01) ◽  
pp. 114-120
Author(s):  
Chaudhary Sarimurrab, Ankita Kesari Naman and Sudha Narang
Keyword(s):  
Random Noise ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Data Generation ◽  
Generative Adversarial Network ◽  
Practical Applications ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Face Generation ◽  
Human Faces

The Generative Models have gained considerable attention in the field of unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to its outstanding data generation capability. Many models of GAN have proposed, and several practical applications emerged in various domains of computer vision and machine learning. Despite GAN's excellent success, there are still obstacles to stable training. In this model, we aim to generate human faces through un-labelled data via the help of Deep Convolutional Generative Adversarial Networks. The applications for generating faces are vast in the field of image processing, entertainment, and other such industries. Our resulting model is successfully able to generate human faces from the given un-labelled data and random noise.

scholarly journals MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hengshi Yu ◽  
Joshua D. Welch
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Data Generation ◽  
Gene Expression Response ◽  
Adversarial Networks ◽  
Cell Gene Expression ◽  
Expression Response ◽  
Cell Gene

AbstractDeep generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs) generate and manipulate high-dimensional images. We systematically assess the complementary strengths and weaknesses of these models on single-cell gene expression data. We also develop MichiGAN, a novel neural network that combines the strengths of VAEs and GANs to sample from disentangled representations without sacrificing data generation quality. We learn disentangled representations of three large single-cell RNA-seq datasets and use MichiGAN to sample from these representations. MichiGAN allows us to manipulate semantically distinct aspects of cellular identity and predict single-cell gene expression response to drug treatment.

scholarly journals Capturing the diversity of biological tuning curves using generative adversarial networks

2017 ◽  
Author(s):  
Takafumi Arakaki ◽  
G. Barello ◽  
Yashar Ahmadian
Keyword(s):  
Machine Learning ◽  
Latent Variables ◽  
Likelihood Function ◽  
Network Models ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Connectivity Matrix ◽  
Model Parameters ◽  
Tuning Curves ◽  
Adversarial Networks

AbstractTuning curves characterizing the response selectivities of biological neurons often exhibit large degrees of irregularity and diversity across neurons. Theoretical network models that feature heterogeneous cell populations or random connectivity also give rise to diverse tuning curves. However, a general framework for fitting such models to experimentally measured tuning curves is lacking. We address this problem by proposing to view mechanistic network models as generative models whose parameters can be optimized to fit the distribution of experimentally measured tuning curves. A major obstacle for fitting such models is that their likelihood function is not explicitly available or is highly intractable to compute. Recent advances in machine learning provide ways for fitting generative models without the need to evaluate the likelihood and its gradient. Generative Adversarial Networks (GAN) provide one such framework which has been successful in traditional machine learning tasks. We apply this approach in two separate experiments, showing how GANs can be used to fit commonly used mechanistic models in theoretical neuroscience to datasets of measured tuning curves. This fitting procedure avoids the computationally expensive step of inferring latent variables, e.g., the biophysical parameters of individual cells or the particular realization of the full synaptic connectivity matrix, and directly learns model parameters which characterize the statistics of connectivity or of single-cell properties. Another strength of this approach is that it fits the entire, joint distribution of experimental tuning curves, instead of matching a few summary statistics picked a priori by the user. More generally, this framework opens the door to fitting theoretically motivated dynamical network models directly to simultaneously or non-simultaneously recorded neural responses.

scholarly journals Density estimation using deep generative neural networks

2021 ◽  
Vol 118 (15) ◽  
pp. e2101344118
Author(s):  
Qiao Liu ◽  
Jiaze Xu ◽  
Rui Jiang ◽  
Wing Hung Wong
Keyword(s):  
Neural Networks ◽  
Density Estimation ◽  
General Purpose ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Data Generation ◽  
Diverse Range ◽  
Statistical Framework ◽  
Adversarial Networks ◽  
Density Values

Density estimation is one of the fundamental problems in both statistics and machine learning. In this study, we propose Roundtrip, a computational framework for general-purpose density estimation based on deep generative neural networks. Roundtrip retains the generative power of deep generative models, such as generative adversarial networks (GANs) while it also provides estimates of density values, thus supporting both data generation and density estimation. Unlike previous neural density estimators that put stringent conditions on the transformation from the latent space to the data space, Roundtrip enables the use of much more general mappings where target density is modeled by learning a manifold induced from a base density (e.g., Gaussian distribution). Roundtrip provides a statistical framework for GAN models where an explicit evaluation of density values is feasible. In numerical experiments, Roundtrip exceeds state-of-the-art performance in a diverse range of density estimation tasks.

scholarly journals Image Augmentation based on GAN deep learning approach with Textual Content Descriptors

2021 ◽  
Vol 3 (3) ◽  
pp. 210-225
Author(s):  
Judy Simon
Keyword(s):  
Computer Vision ◽  
Synthetic Data ◽  
Original Data ◽  
Generative Models ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Data Sets ◽  
Biological Vision ◽  
Adversarial Networks ◽  
Textual Content

Computer vision, also known as computational visual perception, is a branch of artificial intelligence that allows computers to interpret digital pictures and videos in a manner comparable to biological vision. It entails the development of techniques for simulating biological vision. The aim of computer vision is to extract more meaningful information from visual input than that of a biological vision. Computer vision is exploding due to the avalanche of data being produced today. Powerful generative models, such as Generative Adversarial Networks (GANs), are responsible for significant advances in the field of picture creation. The focus of this research is to concentrate on textual content descriptors in the images used by GANs to generate synthetic data from the MNIST dataset to either supplement or replace the original data while training classifiers. This can provide better performance than other traditional image enlarging procedures due to the good handling of synthetic data. It shows that training classifiers on synthetic data are as effective as training them on pure data alone, and it also reveals that, for small training data sets, supplementing the dataset by first training GANs on the data may lead to a significant increase in classifier performance.

GENERATIVE ADVERSARIAL NETWORKS: A SHORT REVIEW

2020 ◽  
Vol 2020 (10) ◽  
pp. 312-1-312-7
Author(s):  
Habib Ullah ◽  
Sultan Daud Khan ◽  
Mohib Ullah ◽  
Maqsood Mahmud ◽  
Faouzi Alaya Cheikh
Keyword(s):  
Short Review ◽  
Generative Models ◽  
Image Resolution ◽  
Generative Adversarial Networks ◽  
Data Generation ◽  
Future Directions ◽  
Adversarial Networks ◽  
Image Translation ◽  
Generation Capacity ◽  
Insight Into

Generative adversarial networks (GANs) have been significantly investigated in the past few years due to its outstanding data generation capacity. The extensive use of the GANs techniques is dominant in the field of computer vision, for example, plausible image generation, image to image translation, facial attribute manipulation, improving image resolution, and image to text translation. In spite of the significant success achieved in these domains, applying GANs to various other problems still presents important challenges. Several reviews and surveys for GANs are available in the literature. However, none of them present short but focused review about the most significant aspects of GANs. In this paper, we address these aspects. We analyze the basic theory of GANs and the differences among various generative models. Then, we discuss the recent spectrum of applications covered by the GANs. We also provide an insight into the challenges and future directions.

scholarly journals Review on Generative Adversarial Networks: Focusing on Computer Vision and Its Applications

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1216
Author(s):  
Sung-Wook Park ◽  
Jae-Sub Ko ◽  
Jun-Ho Huh ◽  
Jong-Chan Kim
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Random Noise ◽  
Image Synthesis ◽  
Research Direction ◽  
Input Image ◽  
Generative Model ◽  
Generative Adversarial Networks ◽  
Future Research ◽  
Adversarial Networks

The emergence of deep learning model GAN (Generative Adversarial Networks) is an important turning point in generative modeling. GAN is more powerful in feature and expression learning compared to machine learning-based generative model algorithms. Nowadays, it is also used to generate non-image data, such as voice and natural language. Typical technologies include BERT (Bidirectional Encoder Representations from Transformers), GPT-3 (Generative Pretrained Transformer-3), and MuseNet. GAN differs from the machine learning-based generative model and the objective function. Training is conducted by two networks: generator and discriminator. The generator converts random noise into a true-to-life image, whereas the discriminator distinguishes whether the input image is real or synthetic. As the training continues, the generator learns more sophisticated synthesis techniques, and the discriminator grows into a more accurate differentiator. GAN has problems, such as mode collapse, training instability, and lack of evaluation matrix, and many researchers have tried to solve these problems. For example, solutions such as one-sided label smoothing, instance normalization, and minibatch discrimination have been proposed. The field of application has also expanded. This paper provides an overview of GAN and application solutions for computer vision and artificial intelligence healthcare field researchers. The structure and principle of operation of GAN, the core models of GAN proposed to date, and the theory of GAN were analyzed. Application examples of GAN such as image classification and regression, image synthesis and inpainting, image-to-image translation, super-resolution and point registration were then presented. The discussion tackled GAN’s problems and solutions, and the future research direction was finally proposed.

scholarly journals Quantum generative adversarial networks with multiple superconducting qubits

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Kaixuan Huang ◽  
Zheng-An Wang ◽  
Chao Song ◽  
Kai Xu ◽  
Hekang Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Scale Up ◽  
Generative Adversarial Networks ◽  
Nash Equilibrium Point ◽  
Practical Applications ◽  
Adversarial Networks ◽  
Experimental Implementation ◽  
Challenging Tasks ◽  
Machine Learning Applications ◽  
Near Term

AbstractGenerative adversarial networks are an emerging technique with wide applications in machine learning, which have achieved dramatic success in a number of challenging tasks including image and video generation. When equipped with quantum processors, their quantum counterparts—called quantum generative adversarial networks (QGANs)—may even exhibit exponential advantages in certain machine learning applications. Here, we report an experimental implementation of a QGAN using a programmable superconducting processor, in which both the generator and the discriminator are parameterized via layers of single- and two-qubit quantum gates. The programmed QGAN runs automatically several rounds of adversarial learning with quantum gradients to achieve a Nash equilibrium point, where the generator can replicate data samples that mimic the ones from the training set. Our implementation is promising to scale up to noisy intermediate-scale quantum devices, thus paving the way for experimental explorations of quantum advantages in practical applications with near-term quantum technologies.

scholarly journals A New Integrated Approach for Landslide Data Balancing and Spatial Prediction Based on Generative Adversarial Networks (GAN)

2021 ◽  
Vol 13 (19) ◽  
pp. 4011
Author(s):  
Husam A. H. Al-Najjar ◽  
Biswajeet Pradhan ◽  
Raju Sarkar ◽  
Ghassan Beydoun ◽  
Abdullah Alamri
Keyword(s):  
Machine Learning ◽  
Spatial Prediction ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Slope Aspect ◽  
Support Vector ◽  
Learning Models ◽  
Adversarial Networks ◽  
Landslide Data ◽  
Machine Learning Models

Landslide susceptibility mapping has significantly progressed with improvements in machine learning techniques. However, the inventory / data imbalance (DI) problem remains one of the challenges in this domain. This problem exists as a good quality landslide inventory map, including a complete record of historical data, is difficult or expensive to collect. As such, this can considerably affect one’s ability to obtain a sufficient inventory or representative samples. This research developed a new approach based on generative adversarial networks (GAN) to correct imbalanced landslide datasets. The proposed method was tested at Chukha Dzongkhag, Bhutan, one of the most frequent landslide prone areas in the Himalayan region. The proposed approach was then compared with the standard methods such as the synthetic minority oversampling technique (SMOTE), dense imbalanced sampling, and sparse sampling (i.e., producing non-landslide samples as many as landslide samples). The comparisons were based on five machine learning models, including artificial neural networks (ANN), random forests (RF), decision trees (DT), k-nearest neighbours (kNN), and the support vector machine (SVM). The model evaluation was carried out based on overall accuracy (OA), Kappa Index, F1-score, and area under receiver operating characteristic curves (AUROC). The spatial database was established with a total of 269 landslides and 10 conditioning factors, including altitude, slope, aspect, total curvature, slope length, lithology, distance from the road, distance from the stream, topographic wetness index (TWI), and sediment transport index (STI). The findings of this study have shown that both GAN and SMOTE data balancing approaches have helped to improve the accuracy of machine learning models. According to AUROC, the GAN method was able to boost the models by reaching the maximum accuracy of ANN (0.918), RF (0.933), DT (0.927), kNN (0.878), and SVM (0.907) when default parameters used. With the optimum parameters, all models performed best with GAN at their highest accuracy of ANN (0.927), RF (0.943), DT (0.923) and kNN (0.889), except SVM obtained the highest accuracy of (0.906) with SMOTE. Our finding suggests that RF balanced with GAN can provide the most reasonable criterion for landslide prediction. This research indicates that landslide data balancing may substantially affect the predictive capabilities of machine learning models. Therefore, the issue of DI in the spatial prediction of landslides should not be ignored. Future studies could explore other generative models for landslide data balancing. By using state-of-the-art GAN, the proposed model can be considered in the areas where the data are limited or imbalanced.

scholarly journals Image Synthesis Based On Feature Description

2021 ◽  
Vol 9 (8) ◽  
pp. 2492-2494
Author(s):  
Rohan Bolusani
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Language Processing ◽  
Image Synthesis ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Feature Representations ◽  
Adversarial Networks ◽  
Text Feature ◽  
Image Modelling

Abstract: Generating realistic images from text is innovative and interesting, but modern-day machine learning models are still far from this goal. With research and development in the field of natural language processing, neural network architectures have been developed to learn discriminative text feature representations. Meanwhile, in the field of machine learning, generative adversarial networks (GANs) have begun to generate extremely accurate images of especially in categories, such as faces, album covers, and room interiors. In this work, the main goal is to develop a neural network to bridge these advances in text and image modelling, by essentially translating characters to pixels the project will demonstrate the capability of generative models by taking detailed text descriptions and generate plausible images. Keywords: Deep Learning, Computer Vision, NLP, Generative Adversarial Networks

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