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Generative Models in Artificial Intelligence and Their Applications

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 April 2022) | Viewed by 46881

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Dr. Mauro Castelli

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NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisbon, Portugal
Interests: machine learning; evolutionary computation; deep learning; pattern recognition; neuro-evolution
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Dr. Luca Manzoni

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Department of Mathematics and Geosciences, University of Trieste, 34127 Trieste, Italy
Interests: genetic programming; evolutionary computation; bioinspired computational models; theoretical computer science; machine learning
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Special Issue Information

Dear Colleagues,

In recent years, artificial intelligence has been used to generate a significant amount of high-quality data, like images, music, and videos. The creation of such a vast amount of synthetic data was made possible due to the improved performance of different machine learning techniques, like artificial neural networks. Considering the increased interest in this area, new techniques for automatic data generation and augmentation were recently proposed. For instance, generative adversarial networks (GANs) and their variants are nowadays popular techniques in this research field. The creation of synthetic data was also achieved with evolutionary-based techniques, for instance in the context of multimedia artifacts creation. This Special Issue aims to collect new contributions in the area of generative models in artificial intelligence, focusing on their applications for addressing complex real-world problems in engineering, medicine, entertainment, manufacturing, optimization, business, and related fields. We kindly invite researchers and practitioners to contribute their high-quality original research or review articles on these topics to this Special Issue.

Dr. Mauro Castelli
Dr. Luca Manzoni
Guest Editors

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Keywords

evolutionary computation
genetic programming
generative models
data generation
data augmentation
images generation
algorithmic music
real-world applications

Published Papers (12 papers)

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Editorial

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3 pages, 156 KiB

Open AccessEditorial

Special Issue: Generative Models in Artificial Intelligence and Their Applications

by Mauro Castelli and Luca Manzoni

Appl. Sci. 2022, 12(9), 4127; https://0-doi-org.brum.beds.ac.uk/10.3390/app12094127 - 20 Apr 2022

Cited by 10 | Viewed by 3297

Abstract

In recent years, artificial intelligence has been used to generate a significant amount of high-quality data, such as images, music, and videos [...] Full article

(This article belongs to the Special Issue Generative Models in Artificial Intelligence and Their Applications)

Research

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16 pages, 5410 KiB

Open AccessArticle

GAN-Based Training of Semi-Interpretable Generators for Biological Data Interpolation and Augmentation

by Anastasios Tsourtis, Georgios Papoutsoglou and Yannis Pantazis

Appl. Sci. 2022, 12(11), 5434; https://0-doi-org.brum.beds.ac.uk/10.3390/app12115434 - 27 May 2022

Cited by 2 | Viewed by 2056

Abstract

Single-cell measurements incorporate invaluable information regarding the state of each cell and its underlying regulatory mechanisms. The popularity and use of single-cell measurements are constantly growing. Despite the typically large number of collected data, the under-representation of important cell (sub-)populations negatively affects down-stream analysis and its robustness. Therefore, the enrichment of biological datasets with samples that belong to a rare state or manifold is overall advantageous. In this work, we train families of generative models via the minimization of Rényi divergence resulting in an adversarial training framework. Apart from the standard neural network-based models, we propose families of semi-interpretable generative models. The proposed models are further tailored to generate realistic gene expression measurements, whose characteristics include zero-inflation and sparsity, without the need of any data pre-processing. Explicit factors of the data such as measurement time, state or cluster are taken into account by our generative models as conditional variables. We train the proposed conditional models and compare them against the state-of-the-art on a range of synthetic and real datasets and demonstrate their ability to accurately perform data interpolation and augmentation. Full article

(This article belongs to the Special Issue Generative Models in Artificial Intelligence and Their Applications)

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10 pages, 1208 KiB

Open AccessArticle

Generative Model Using Knowledge Graph for Document-Grounded Conversations

by Boeun Kim, Dohaeng Lee, Damrin Kim, Hongjin Kim, Sihyung Kim, Ohwoog Kwon and Harksoo Kim

Appl. Sci. 2022, 12(7), 3367; https://0-doi-org.brum.beds.ac.uk/10.3390/app12073367 - 25 Mar 2022

Cited by 2 | Viewed by 2960

Abstract

Document-grounded conversation (DGC) is a natural language generation task to generate fluent and informative responses by leveraging dialogue history and document(s). Recently, DGCs have focused on fine-tuning using pretrained language models. However, these approaches have a problem in that they must leverage the background knowledge under capacity constraints. For example, the maximum length of the input is limited to 512 or 1024 tokens. This problem is fatal in DGC because most documents are longer than the maximum input length. To address this problem, we propose a document-grounded generative model using a knowledge graph. The proposed model converts knowledge sentences extracted from the given document(s) into knowledge graphs and fine-tunes the pretrained model using the graph. We validated the effectiveness of the proposed model using a comparative experiment on the well-known Wizard-of-Wikipedia dataset. The proposed model outperformed the previous state-of-the-art model in our experiments on the Doc2dial dataset. Full article

(This article belongs to the Special Issue Generative Models in Artificial Intelligence and Their Applications)

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16 pages, 5634 KiB

Open AccessArticle

Thermal Image Generation for Robust Face Recognition

by Vicente Pavez, Gabriel Hermosilla, Francisco Pizarro, Sebastián Fingerhuth and Daniel Yunge

Appl. Sci. 2022, 12(1), 497; https://0-doi-org.brum.beds.ac.uk/10.3390/app12010497 - 05 Jan 2022

Cited by 4 | Viewed by 3496

Abstract

This article shows how to create a robust thermal face recognition system based on the FaceNet architecture. We propose a method for generating thermal images to create a thermal face database with six different attributes (frown, glasses, rotation, normal, vocal, and smile) based on various deep learning models. First, we use StyleCLIP, which oversees manipulating the latent space of the input visible image to add the desired attributes to the visible face. Second, we use the GANs N’ Roses (GNR) model, a multimodal image-to-image framework. It uses maps of style and content to generate thermal imaging from visible images, using generative adversarial approaches. Using the proposed generator system, we create a database of synthetic thermal faces composed of more than 100k images corresponding to 3227 individuals. When trained and tested using the synthetic database, the Thermal-FaceNet model obtained a 99.98% accuracy. Furthermore, when tested with a real database, the accuracy was more than 98%, validating the proposed thermal images generator system. Full article

(This article belongs to the Special Issue Generative Models in Artificial Intelligence and Their Applications)

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13 pages, 321 KiB

Open AccessFeature PaperArticle

Algorithmic Music for Therapy: Effectiveness and Perspectives

by Alfredo Raglio, Paola Baiardi, Giuseppe Vizzari, Marcello Imbriani, Mauro Castelli, Sara Manzoni, Francisco Vico and Luca Manzoni

Appl. Sci. 2021, 11(19), 8833; https://0-doi-org.brum.beds.ac.uk/10.3390/app11198833 - 23 Sep 2021

Cited by 7 | Viewed by 2930

Abstract

This study assessed the short-term effects of conventional (i.e., human-composed) and algorithmic music on the relaxation level. It also investigated whether algorithmic compositions are perceived as music and are distinguishable from human-composed music. Three hundred twenty healthy volunteers were recruited and randomly allocated to two groups where they listened to either their preferred music or algorithmic music. Another 179 healthy subjects were allocated to four listening groups that respectively listened to: music composed and performed by a human, music composed by a human and performed by a machine; music composed by a machine and performed by a human, music composed and performed by a machine. In the first experiment, participants underwent one of the two music listening conditions—preferred or algorithmic music—in a comfortable state. In the second one, participants were asked to evaluate, through an online questionnaire, the musical excerpts they listened to. The Visual Analogue Scale was used to evaluate their relaxation levels before and after the music listening experience. Other outcomes were evaluated through the responses to the questionnaire. The relaxation level obtained with the music created by the algorithms is comparable to the one achieved with preferred music. Statistical analysis shows that the relaxation level is not affected by the composer, the performer, or the existence of musical training. On the other hand, the perceived effect is related to the performer. Finally, music composed by an algorithm and performed by a human is not distinguishable from that composed by a human. Full article

(This article belongs to the Special Issue Generative Models in Artificial Intelligence and Their Applications)

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23 pages, 14332 KiB

Open AccessArticle

Adversarial Data Augmentation on Breast MRI Segmentation

by João F. Teixeira, Mariana Dias, Eva Batista, Joana Costa, Luís F. Teixeira and Hélder P. Oliveira

Appl. Sci. 2021, 11(10), 4554; https://0-doi-org.brum.beds.ac.uk/10.3390/app11104554 - 17 May 2021

Cited by 4 | Viewed by 2340

Abstract

The scarcity of balanced and annotated datasets has been a recurring problem in medical image analysis. Several researchers have tried to fill this gap employing dataset synthesis with adversarial networks (GANs). Breast magnetic resonance imaging (MRI) provides complex, texture-rich medical images, with the same annotation shortage issues, for which, to the best of our knowledge, no previous work tried synthesizing data. Within this context, our work addresses the problem of synthesizing breast MRI images from corresponding annotations and evaluate the impact of this data augmentation strategy on a semantic segmentation task. We explored variations of image-to-image translation using conditional GANs, namely fitting the generator’s architecture with residual blocks and experimenting with cycle consistency approaches. We studied the impact of these changes on visual verisimilarity and how an U-Net segmentation model is affected by the usage of synthetic data. We achieved sufficiently realistic-looking breast MRI images and maintained a stable segmentation score even when completely replacing the dataset with the synthetic set. Our results were promising, especially when concerning to Pix2PixHD and Residual CycleGAN architectures. Full article

(This article belongs to the Special Issue Generative Models in Artificial Intelligence and Their Applications)

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25 pages, 715 KiB

Open AccessArticle

Using Formal Grammars as Musical Genome

by David D. Albarracín-Molina, Alfredo Raglio, Francisco Rivas-Ruiz and Francisco J. Vico

Appl. Sci. 2021, 11(9), 4151; https://0-doi-org.brum.beds.ac.uk/10.3390/app11094151 - 01 May 2021

Cited by 2 | Viewed by 2309

Abstract

In this paper, we explore a generative music method that can compose atonal and tonal music in different styles. One of the main differences between regular engineering problems and artistic expressions is that goals and constraints are usually ill-defined in the latter case; in fact the rules here could or should be transgressed more regularly. For this reason, our approach does not use a pre-existing dataset to imitate or extract rules from. Instead, it uses formal grammars as a representation method than can retain just the basic features, common to any form of music (e.g., the appearance of rhythmic patterns, the evolution of tone or dynamics during the composition, etc.). Exploring different musical spaces is the responsibility of a program interface that translates musical specifications into the fitness function of a genetic algorithm. This function guides the evolution of those basic features enabling the emergence of novel content. In this study, we then assess the outcome of a particular music specification (guitar ballad) in a controlled real-world setup. As a result, the generated music can be considered similar to human-composed music from a perceptual perspective. This endorses our approach to tackle arts algorithmically, as it is able to produce novel content that complies with human expectations. Full article

(This article belongs to the Special Issue Generative Models in Artificial Intelligence and Their Applications)

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19 pages, 2275 KiB

Open AccessArticle

Generating Synthetic Fermentation Data of Shindari, a Traditional Jeju Beverage, Using Multiple Imputation Ensemble and Generative Adversarial Networks

by Debapriya Hazra and Yung-Cheol Byun

Appl. Sci. 2021, 11(6), 2787; https://0-doi-org.brum.beds.ac.uk/10.3390/app11062787 - 20 Mar 2021

Cited by 6 | Viewed by 2536

Abstract

Fermentation is an age-old technique used to preserve food by restoring proper microbial balance. Boiled barley and nuruk are fermented for a short period to produce Shindari, a traditional beverage for the people of Jeju, South Korea. Shindari has been proven to be a drink of multiple health benefits if fermented for an optimal period. It is necessary to predict the ideal fermentation time required by each microbial community to keep the advantages of the microorganisms produced by the fermentation process in Shindari intact and to eliminate contamination. Prediction through machine learning requires past data but the process of obtaining fermentation data of Shindari is time consuming, expensive, and not easily available. Therefore, there is a need to generate synthetic fermentation data to explore various benefits of the drink and to reduce any risk from overfermentation. In this paper, we propose a model that takes incomplete tabular fermentation data of Shindari as input and uses multiple imputation ensemble (MIE) and generative adversarial networks (GAN) to generate synthetic fermentation data that can be later used for prediction and microbial spoilage control. For multiple imputation, we used multivariate imputation by chained equations and random forest imputation, and ensembling was done using the bagging and stacking method. For generating synthetic data, we remodeled the tabular GAN with skip connections and adapted the architecture of Wasserstein GAN with gradient penalty. We compared the performance of our model with other imputation and ensemble models using various evaluation metrics and visual representations. Our GAN model could overcome the mode collapse problem and converged at a faster rate than existing GAN models for synthetic data generation. Experiment results show that our proposed model executes with less error, is more accurate, and generates significantly better synthetic fermentation data compared to other models. Full article

(This article belongs to the Special Issue Generative Models in Artificial Intelligence and Their Applications)

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18 pages, 5104 KiB

Open AccessArticle

Fake It Till You Make It: Guidelines for Effective Synthetic Data Generation

by Fida K. Dankar and Mahmoud Ibrahim

Appl. Sci. 2021, 11(5), 2158; https://doi.org/10.3390/app11052158 - 28 Feb 2021

Cited by 38 | Viewed by 9966

Abstract

Synthetic data provides a privacy protecting mechanism for the broad usage and sharing of healthcare data for secondary purposes. It is considered a safe approach for the sharing of sensitive data as it generates an artificial dataset that contains no identifiable information. Synthetic data is increasing in popularity with multiple synthetic data generators developed in the past decade, yet its utility is still a subject of research. This paper is concerned with evaluating the effect of various synthetic data generation and usage settings on the utility of the generated synthetic data and its derived models. Specifically, we investigate (i) the effect of data pre-processing on the utility of the synthetic data generated, (ii) whether tuning should be applied to the synthetic datasets when generating supervised machine learning models, and (iii) whether sharing preliminary machine learning results can improve the synthetic data models. Lastly, (iv) we investigate whether one utility measure (Propensity score) can predict the accuracy of the machine learning models generated from the synthetic data when employed in real life. We use two popular measures of synthetic data utility, propensity score and classification accuracy, to compare the different settings. We adopt a recent mechanism for the calculation of propensity, which looks carefully into the choice of model for the propensity score calculation. Accordingly, this paper takes a new direction with investigating the effect of various data generation and usage settings on the quality of the generated data and its ensuing models. The goal is to inform on the best strategies to follow when generating and using synthetic data. Full article

(This article belongs to the Special Issue Generative Models in Artificial Intelligence and Their Applications)

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17 pages, 8331 KiB

Open AccessArticle

Daydriex: Translating Nighttime Scenes towards Daytime Driving Experience at Night

by Euihyeok Lee and Seungwoo Kang

Appl. Sci. 2021, 11(5), 2013; https://0-doi-org.brum.beds.ac.uk/10.3390/app11052013 - 25 Feb 2021

Cited by 1 | Viewed by 1933

Abstract

What if the window of our cars is a magic window, which transforms dark views outside of the window at night into bright ones as we can see in the daytime? To realize such a window, one of important requirements is that the stream of transformed images displayed on the window should be of high quality so that users perceive it as real scenes in the day. Although image-to-image translation techniques based on Generative Adversarial Networks (GANs) have been widely studied, night-to-day image translation is still a challenging task. In this paper, we propose Daydriex, a processing pipeline to generate enhanced daytime translation focusing on road views. Our key idea is to supplement the missing information in dark areas of input image frames by using existing daytime images corresponding to the input images from street view services. We present a detailed processing flow and address several issues to realize our idea. Our evaluation shows that the results by Daydriex achieves lower Fréchet Inception Distance (FID) scores and higher user perception scores compared to those by CycleGAN only. Full article

(This article belongs to the Special Issue Generative Models in Artificial Intelligence and Their Applications)

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Review

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21 pages, 2631 KiB

Open AccessReview

Survey on Implementations of Generative Adversarial Networks for Semi-Supervised Learning

by Ali Reza Sajun and Imran Zualkernan

Appl. Sci. 2022, 12(3), 1718; https://0-doi-org.brum.beds.ac.uk/10.3390/app12031718 - 07 Feb 2022

Cited by 20 | Viewed by 3632

Abstract

Given recent advances in deep learning, semi-supervised techniques have seen a rise in interest. Generative adversarial networks (GANs) represent one recent approach to semi-supervised learning (SSL). This paper presents a survey method using GANs for SSL. Previous work in applying GANs to SSL are classified into pseudo-labeling/classification, encoder-based, TripleGAN-based, two GAN, manifold regularization, and stacked discriminator approaches. A quantitative and qualitative analysis of the various approaches is presented. The R3-CGAN architecture is identified as the GAN architecture with state-of-the-art results. Given the recent success of non-GAN-based approaches for SSL, future research opportunities involving the adaptation of elements of SSL into GAN-based implementations are also identified. Full article

(This article belongs to the Special Issue Generative Models in Artificial Intelligence and Their Applications)

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39 pages, 3359 KiB

Open AccessFeature PaperReview

Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development

by Fernando Peres and Mauro Castelli

Appl. Sci. 2021, 11(14), 6449; https://0-doi-org.brum.beds.ac.uk/10.3390/app11146449 - 13 Jul 2021

Cited by 35 | Viewed by 6232

Abstract

In the past few decades, metaheuristics have demonstrated their suitability in addressing complex problems over different domains. This success drives the scientific community towards the definition of new and better-performing heuristics and results in an increased interest in this research field. Nevertheless, new studies have been focused on developing new algorithms without providing consolidation of the existing knowledge. Furthermore, the absence of rigor and formalism to classify, design, and develop combinatorial optimization problems and metaheuristics represents a challenge to the field’s progress. This study discusses the main concepts and challenges in this area and proposes a formalism to classify, design, and code combinatorial optimization problems and metaheuristics. We believe these contributions may support the progress of the field and increase the maturity of metaheuristics as problem solvers analogous to other machine learning algorithms. Full article

(This article belongs to the Special Issue Generative Models in Artificial Intelligence and Their Applications)

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