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Dissertations / Theses on the topic 'Generative adversarial networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Wang, Zesen. "Generative Adversarial Networks in Text Generation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264575.

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The Generative Adversarial Network (GAN) was firstly proposed in 2014, and it has been highly studied and developed in recent years. It has obtained great success in the problems that cannot be explicitly defined by a math equation such as generating real images. However, since the GAN was initially designed to solve the problem in a continuous domain (image generation, for example), the performance of GAN in text generation is developing because the sentences are naturally discrete (no interpolation exists between “hello" and “bye"). In the thesis, it firstly introduces fundamental concepts i

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2

Daley, Jr John. "Generating Synthetic Schematics with Generative Adversarial Networks." Thesis, Högskolan Kristianstad, Fakulteten för naturvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-20901.

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This study investigates synthetic schematic generation using conditional generative adversarial networks, specifically the Pix2Pix algorithm was implemented for the experimental phase of the study. With the increase in deep neural network’s capabilities and availability, there is a demand for verbose datasets. This in combination with increased privacy concerns, has led to synthetic data generation utilization. Analysis of synthetic images was completed using a survey. Blueprint images were generated and were successful in passing as genuine images with an accuracy of 40%. This study confirms

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Berman, Alan. "Generative adversarial networks for fine art generation." Master's thesis, University of Cape Town, 2020. http://hdl.handle.net/11427/32458.

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Generative Adversarial Networks (GANs), a generative modelling technique most commonly used for image generation, have recently been applied to the task of fine art generation. Wasserstein GANs and GANHack techniques have not been applied in GANs that generate fine art, despite their showing improved GAN results in other applications. This thesis investigates whether Wasserstein GANs and GANHack extensions to DCGANs can improve the quality of DCGAN-based fine art generation. There is also no accepted method of evaluating or comparing GANs for fine art generation. DCGAN's, Wasserstein GANs' and

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Zeid, Baker Mousa. "Generation of Synthetic Images with Generative Adversarial Networks." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15866.

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Machine Learning is a fast growing area that revolutionizes computer programs by providing systems with the ability to automatically learn and improve from experience. In most cases, the training process begins with extracting patterns from data. The data is a key factor for machine learning algorithms, without data the algorithms will not work. Thus, having sufficient and relevant data is crucial for the performance. In this thesis, the researcher tackles the problem of not having a sufficient dataset, in terms of the number of training examples, for an image classification task. The idea is

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Haiderbhai, Mustafa. "Generating Synthetic X-rays Using Generative Adversarial Networks." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41092.

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We propose a novel method for generating synthetic X-rays from atypical inputs. This method creates approximate X-rays for use in non-diagnostic visualization problems where only generic cameras and sensors are available. Traditional methods are restricted to 3-D inputs such as meshes or Computed Tomography (CT) scans. We create custom synthetic X-ray datasets using a custom generator capable of creating RGB images, point cloud images, and 2-D pose images. We create a dataset using natural hand poses and train general-purpose Conditional Generative Adversarial Networks (CGANs) as well as our o

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Garcia, Torres Douglas. "Generation of Synthetic Data with Generative Adversarial Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254366.

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The aim of synthetic data generation is to provide data that is not real for cases where the use of real data is somehow limited. For example, when there is a need for larger volumes of data, when the data is sensitive to use, or simply when it is hard to get access to the real data. Traditional methods of synthetic data generation use techniques that do not intend to replicate important statistical properties of the original data. Properties such as the distribution, the patterns or the correlation between variables, are often omitted. Moreover, most of the existing tools and approaches requi

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Graffieti, Gabriele. "Style Transfer with Generative Adversarial Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17015/.

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This dissertation is focused on trying to use concepts from style transfer and image-to-image translation to address the problem of defogging. Defogging (or dehazing) is the ability to remove fog from an image, restoring it as if the photograph was taken during optimal weather conditions. The task of defogging is of particular interest in many fields, such as surveillance or self driving cars. In this thesis an unpaired approach to defogging is adopted, trying to translate a foggy image to the correspondent clear picture without having pairs of foggy and ground truth haze-free images during

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Aftab, Nadeem. "Disocclusion Inpainting using Generative Adversarial Networks." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-40502.

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The old methods used for images inpainting of the Depth Image Based Rendering (DIBR) process are inefficient in producing high-quality virtual views from captured data. From the viewpoint of the original image, the generated data’s structure seems less distorted in the virtual view obtained by translation but when then the virtual view involves rotation, gaps and missing spaces become visible in the DIBR generated data. The typical approaches for filling the disocclusion tend to be slow, inefficient, and inaccurate. In this project, a modern technique Generative Adversarial Network (GAN) is us

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Paget, Bryan. "An Introduction to Generative Adversarial Networks." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39603.

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Daniel, Filippo <1995&gt. "Transfer learning with generative adversarial networks." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/16989.

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Generative Adversarial Networks (GANs) emerged in recent years as the undiscussed SotA for image synthesis. This model leverages the recent successes of convolutional networks in the field of computer vision to learn the probability distribution of image datasets. Following the first proposal of GANs, many developments and usages of the models have been proposed. This thesis aims to review the evolution of the model and use one of the most recent variations to generate realistic portrait images with a targeted set of features. The usage of this model will be applied in a transfer learning appr

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Fan, Zijian. "Applying Generative Adversarial Networks for the Generation of Adversarial Attacks Against Continuous Authentication." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289634.

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Cybersecurity has been a hot topic over the past decades with lots of approaches being proposed to secure our private information. One of the emerging approaches in security is continuous authentication, in which the computer system is authenticating the user by monitoring the user behavior during the login session. Although the research of continuous authentication has got a significant achievement, the security of state-of-the-art continuous authentication systems is far from perfect. In this thesis, we explore the ability of classifiers used in continuous authentication and examine whether

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Beyki, Mohammad Reza. "Synthetic Electronic Medical Record Generation using Generative Adversarial Networks." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104642.

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It has been a while that computers have replaced our record books, and medical records are no exception. Electronic Health Records (EHR) are digital version of a patient's medical records. EHRs are available to authorized users, and they contain the medical records of the patient, which should help doctors understand a patient's condition quickly. In recent years, Deep Learning models have proved their value and have become state-of-the-art in computer vision, natural language processing, speech and other areas. The private nature of EHR data has prevented public access to EHR datasets. There

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Oskarsson, Joel. "Probabilistic Regression using Conditional Generative Adversarial Networks." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166637.

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Regression is a central problem in statistics and machine learning with applications everywhere in science and technology. In probabilistic regression the relationship between a set of features and a real-valued target variable is modelled as a conditional probability distribution. There are cases where this distribution is very complex and not properly captured by simple approximations, such as assuming a normal distribution. This thesis investigates how conditional Generative Adversarial Networks (GANs) can be used to properly capture more complex conditional distributions. GANs have seen gr

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Egan, Nicholas R. (Nicholas Ryan). "Natural video synthesis with Generative Adversarial Networks." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123076.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 71-74).<br>Generative Adversarial Networks (GANs) are the state of the art neural network models for image generation, but the use of GANs for video generation is still largely unexplored. This thesis introduces new GAN based video generation methods by proposing the technique of model inflation and the segmentation-to-video task. The model inflation technique converts image generative mode

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Nataraj, Vismitha, and Sushmitha Narayanan. "Resolving Class Imbalance using Generative Adversarial Networks." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-41405.

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Brodie, Michael B. "Methods for Generative Adversarial Output Enhancement." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8763.

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Generative Adversarial Networks (GAN) learn to synthesize novel samples for a given data distribution. While GANs can train on diverse data of various modalities, the most successful use cases to date apply GANs to computer vision tasks. Despite significant advances in training algorithms and network architectures, GANs still struggle to consistently generate high-quality outputs after training. We present a series of papers that improve GAN output inference qualitatively and quantitatively. The first chapter, Alpha Model Domination, addresses a related subfield of Multiple Choice Learning, wh

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Yamazaki, Hiroyuki Vincent. "On Depth and Complexity of Generative Adversarial Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217293.

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Although generative adversarial networks (GANs) have achieved state-of-the-art results in generating realistic look- ing images, they are often parameterized by neural net- works with relatively few learnable weights compared to those that are used for discriminative tasks. We argue that this is suboptimal in a generative setting where data is of- ten entangled in high dimensional space and models are ex- pected to benefit from high expressive power. Additionally, in a generative setting, a model often needs to extrapo- late missing information from low dimensional latent space when generating

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Westberg, Simon. "Investigating the Learning Behavior of Generative Adversarial Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301315.

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Since their introduction in 2014, generative adversarial networks (GANs) have quickly become one of the most popular and successful frameworks for training deep generative models. GANs have shown exceptional results on different image generation tasks and they are known for their ability to produce realistic high- resolution images. However, despite the success and widespread use of the GAN framework, the models can be highly unstable to train and the training process has been shown to be extremely sensitive to hyperparameter settings and network architectures. In this thesis, we investigate h

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Desentz, Derek. "Partial Facial Re-imaging Using Generative Adversarial Networks." Wright State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright1622122813797895.

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Ankaräng, Fredrik. "Generative Adversarial Networks for Cross-Lingual Voice Conversion." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299560.

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Speech synthesis is a technology that increasingly influences our daily lives, in the form of smart assistants, advanced translation systems and similar applications. In this thesis, the phenomenon of making one’s voice sound like the voice of someone else is explored. This topic is called voice conversion and needs to be done without altering the linguistic content of speech. More specifically, a Cycle-Consistent Adversarial Network that has proven to work well in a monolingual setting, is evaluated in a multilingual environment. The model is trained to convert voices between native speakers

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Ljung, Mikael. "Synthetic Data Generation for the Financial Industry Using Generative Adversarial Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301307.

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Following the introduction of new laws and regulations to ensure data protection in GDPR and PIPEDA, interests in technologies to protect data privacy have increased. A promising research trajectory in this area is found in Generative Adversarial Networks (GAN), an architecture trained to produce data that reflects the statistical properties of its underlying dataset without compromising the integrity of the data subjects. Despite the technology’s young age, prior research has made significant progress in the generation process of so-called synthetic data, and the current models can generate i

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Karlsson, Anton, and Torbjörn Sjöberg. "Synthesis of Tabular Financial Data using Generative Adversarial Networks." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273633.

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Digitalization has led to tons of available customer data and possibilities for data-driven innovation. However, the data needs to be handled carefully to protect the privacy of the customers. Generative Adversarial Networks (GANs) are a promising recent development in generative modeling. They can be used to create synthetic data which facilitate analysis while ensuring that customer privacy is maintained. Prior research on GANs has shown impressive results on image data. In this thesis, we investigate the viability of using GANs within the financial industry. We investigate two state-of-the-

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Castillo, Araújo Victor. "Ensembles of Single Image Super-Resolution Generative Adversarial Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290945.

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Generative Adversarial Networks have been used to obtain state-of-the-art results for low-level computer vision tasks like single image super-resolution, however, they are notoriously difficult to train due to the instability related to the competing minimax framework. Additionally, traditional ensembling mechanisms cannot be effectively applied with these types of networks due to the resources they require at inference time and the complexity of their architectures. In this thesis an alternative method to create ensembles of individual, more stable and easier to train, models by using interpo

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Liu, Jiaping. "A Study on Distribution Learning of Generative Adversarial Networks." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41250.

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This thesis is an exploration of the properties of shallow generative adversarial networks (GANs). We focus on several aspects of GANs to investigate the learnability of a class of distributions using shallow GANs and conduct experiments to explore the influence of these aspects on the performance of the GAN models. We identify and analyze several pathological phenomena in theoretical analysis and experiments, and propose potential solutions for them.

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Radhakrishnan, Saieshwar. "Domain Adaptation of IMU sensors using Generative Adversarial Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-286821.

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Autonomous vehicles rely on sensors for a clear understanding of the environment and in a heavy duty truck, the sensors are placed at multiple locations like the cabin, chassis and the trailer in order to increase the field of view and reduce the blind spot area. Usually, these sensors perform best when they are stationary relative to the ground, hence large and fast movements, which are quite common in a truck, may lead to performance reduction, erroneous data or in the worst case, a sensor failure. This enforces a need to validate the sensors before using them for making life-critical decisi

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Sheriff, Waseem. "Learning to predict text quality using Generative Adversarial Networks." Thesis, KTH, Numerisk analys, NA, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264109.

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Generating summaries of long text articles is a common application in natural language processing. Automatic text summarization models often find themselves generating summaries that don’t resemble the quality of human written text, even though they preserve factual accuracy. In this thesis, a method to improve quality of summaries is created by combining loss functions from an existing baseline competitive model (Pointer Generator Networks) for abstractive text summarization with SeqGAN - a successful text generation algorithm based on Generative Adversarial Networks. The model is tested on t

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Birgersson, Anna, and Klara Hellgren. "Texture Enhancement in 3D Maps using Generative Adversarial Networks." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-162446.

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In this thesis we investigate the use of GANs for texture enhancement. To achievethis, we have studied if synthetic satellite images generated by GANs will improvethe texture in satellite-based 3D maps. We investigate two GANs; SRGAN and pix2pix. SRGAN increases the pixelresolution of the satellite images by generating upsampled images from low resolutionimages. As for pip2pix, the GAN performs image-to-image translation bytranslating a source image to a target image, without changing the pixel resolution. We trained the GANs in two different approaches, named SAT-to-AER andSAT-to-AER-3D, wher

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Nistal, Hurlé Javier. "Exploring generative adversarial networks for controllable musical audio synthesis." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT009.

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Les synthétiseurs audio sont des instruments de musique électroniques qui génèrent des sons artificiels sous un certain contrôle paramétrique. Alors que les synthétiseurs ont évolué depuis leur popularisation dans les années 70, deux défis fondamentaux restent encore non résolus: 1) le développement de systèmes de synthèse répondant à des paramètres sémantiquement intuitifs; 2) la conception de techniques de synthèse «universelles», indépendantes de la source à modéliser. Cette thèse étudie l’utilisation des réseaux adversariaux génératifs (ou GAN) pour construire de tels systèmes. L’objectif

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Evholt, David, and Oscar Larsson. "Generative Adversarial Networks and Natural Language Processing for Macroeconomic Forecasting." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273422.

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Macroeconomic forecasting is a classic problem, today most often modeled using time series analysis. Few attempts have been made using machine learning methods, and even fewer incorporating unconventional data, such as that from social media. In this thesis, a Generative Adversarial Network (GAN) is used to predict U.S. unemployment, beating the ARIMA benchmark on all horizons. Furthermore, attempts at using Twitter data and the Natural Language Processing (NLP) model DistilBERT are performed. While these attempts do not beat the benchmark, they do show promising results with predictive power.

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Stenhagen, Petter. "Improving Realism in Synthetic Barcode Images using Generative Adversarial Networks." Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151959.

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This master thesis explores the possibility of using generative Adversarial Networks (GANs) to refine labeled synthetic code images to resemble real code images while preserving label information. The GAN used in this thesis consists of a refiner and a discriminator. The discriminator tries to distinguish between real images and refined synthetic images. The refiner tries to fool the discriminator by producing refined synthetic images such that the discriminator classify them as real. By updating these two networks iteratively, the idea is that they will push each other to get better, resultin

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Hagvall, Hörnstedt Julia. "Synthesis of Thoracic Computer Tomography Images using Generative Adversarial Networks." Thesis, Linköpings universitet, Avdelningen för medicinsk teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158280.

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The use of machine learning algorithms to enhance and facilitate medical diagnosis and analysis is a promising and an important area, which could improve the workload of clinicians’ substantially. In order for machine learning algorithms to learn a certain task, large amount of data needs to be available. Data sets for medical image analysis are rarely public due to restrictions concerning the sharing of patient data. The production of synthetic images could act as an anonymization tool to enable the distribution of medical images and facilitate the training of machine learning algorithms, whi

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Zou, Xiaozhou. "Improve the Convergence Speed and Stability of Generative Adversarial Networks." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1309.

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In this thesis, we address two major problems in Generative Adversarial Networks (GAN), an important sub-field in deep learning. The first problem that we address is the instability in the training process that happens in many real-world problems and the second problem that we address is the lack of a good evaluation metric for the performance of GAN algorithms. To understand and address the first problem, three approaches are developed. Namely, we introduce randomness to the training process; we investigate various normalization methods; most importantly we develop a better parameter initiali

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De, Biase Alessia. "Generative Adversarial Networks to enhance decision support in digital pathology." Thesis, Linköpings universitet, Statistik och maskininlärning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158486.

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Histopathological evaluation and Gleason grading on Hematoxylin and Eosin(H&E) stained specimens is the clinical standard in grading prostate cancer. Recently, deep learning models have been trained to assist pathologists in detecting prostate cancer. However, these predictions could be improved further regarding variations in morphology, staining and differences across scanners. An approach to tackle such problems is to employ conditional GANs for style transfer. A total of 52 prostatectomies from 48 patients were scanned with two different scanners. Data was split into 40 images for trai

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Lavault, Antoine. "Generative Adversarial Networks for Synthesis and Control of Drum Sounds." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS614.

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Les synthétiseurs audio sont des systèmes électroniques capable de générer des sons artificiels sous un ensemble de paramètres dépendants de leur architecture. Quand bien même de multiples évolutions ont transformé les synthétiseurs de simples curiosités sonores dans les années 60 et précédentes à des instruments maîtres dans les productions musicales modernes, deux grands défis restent à relever: le développement d'un système de synthèse répondant à des paramètres cohérent avec leur perception par un humain et la conception d'une méthode de synthèse universelle, capable de modéliser n'importe

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Berglöf, Olle, and Adam Jacobs. "Effects of Transfer Learning on Data Augmentation with Generative Adversarial Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259485.

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Data augmentation is a technique that acquires more training data by augmenting available samples, where the training data is used to fit model parameters. Data augmentation is utilized due to a shortage of training data in certain domains and to reduce overfitting. Augmenting a training dataset for image classification with a Generative Adversarial Network (GAN) has been shown to increase classification accuracy. This report investigates if transfer learning within a GAN can further increase classification accuracy when utilizing the augmented training dataset. The method section describes a

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Gawande, Saurabh. "Generative adversarial networks for single image super resolution in microscopy images." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230188.

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Image Super resolution is a widely-studied problem in computer vision, where the objective is to convert a lowresolution image to a high resolution image. Conventional methods for achieving super-resolution such as image priors, interpolation, sparse coding require a lot of pre/post processing and optimization. Recently, deep learning methods such as convolutional neural networks and generative adversarial networks are being used to perform super-resolution with results competitive to the state of the art but none of them have been used on microscopy images. In this thesis, a generative advers

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Delacruz, Gian P. "Using Generative Adversarial Networks to Classify Structural Damage Caused by Earthquakes." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2158.

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The amount of structural damage image data produced in the aftermath of an earthquake can be staggering. It is challenging for a few human volunteers to efficiently filter and tag these images with meaningful damage information. There are several solution to automate post-earthquake reconnaissance image tagging using Machine Learning (ML) solutions to classify each occurrence of damage per building material and structural member type. ML algorithms are data driven; improving with increased training data. Thanks to the vast amount of data available and advances in computer architectures, ML and

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Thaung, Ludwig. "Advanced Data Augmentation : With Generative Adversarial Networks and Computer-Aided Design." Thesis, Linköpings universitet, Datorseende, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-170886.

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CNN-based (Convolutional Neural Network) visual object detectors often reach human level of accuracy but need to be trained with large amounts of manually annotated data. Collecting and annotating this data can frequently be time-consuming and financially expensive. Using generative models to augment the data can help minimize the amount of data required and increase detection per-formance. Many state-of-the-art generative models are Generative Adversarial Networks (GANs). This thesis investigates if and how one can utilize image data to generate new data through GANs to train a YOLO-based (Yo

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Hinz, Tobias [Verfasser]. "Disentanglement, Compositionality, Specification: Representation Learning with Generative Adversarial Networks / Tobias Hinz." Hamburg : Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky, 2021. http://d-nb.info/1234150344/34.

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GIANSANTI, VALENTINA. "Integration of heterogeneous single cell data with Wasserstein Generative Adversarial Networks." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2023. https://hdl.handle.net/10281/404516.

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Tessuti, organi e organismi sono sistemi biologici complessi, oggetto di studi che mirano alla caratterizzazione dei loro processi biologici. Comprendere il loro funzionamento e la loro interazione in campioni sani e malati consente di interferire, correggere e prevenire le disfunzioni dalle quali si sviluppano possibilmente le malattie. I recenti sviluppi nelle tecnologie di sequenziamento single-cell stanno ampliano la capacità di profilare, a livello di singola cellula, diversi layer molecolari (trascrittoma, genoma, epigenoma, proteoma). Il numero, la grandezza e le diverse modalità dei da

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Bartocci, John Timothy. "Generating a synthetic dataset for kidney transplantation using generative adversarial networks and categorical logit encoding." Bowling Green State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1617104572023027.

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Paiano, Michele. "Sperimentazione di tools per la creazione e l'addestramento di Generative Adversarial Networks." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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Soggetto principale di questo lavoro saranno le reti neurali artificiali, ed in particolare una classe di modelli generativi detti ”Generative adversarial Net-works”. Questi rappresentano un trampolino di lancio verso la costruzione di sistemi di Intelligenza Artificiale in grado di consumare dati grezzi provenienti dal mondo reale e automaticamente estrarne una comprensione che rappresenta la struttura intrinseca del mondo. Questo costituisce un grande passo avanti rispetto ai sistemi usati in passato, che erano in grado di apprendere da dati di addestramento accuratamente pre-etichettati da

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Albertazzi, Riccardo. "A study on the application of generative adversarial networks to industrial OCR." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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High performance and nearly perfect accuracy are the standards required by OCR algorithms for industrial applications. In the last years research on Deep Learning has proven that Convolutional Neural Networks (CNNs) are a very powerful and robust tool for image analysis and classification; when applied to OCR tasks, CNNs are able to perform much better than previously adopted techniques and reach easily 99% accuracy. However, Deep Learning models' effectiveness relies on the quality of the data used to train them; this can become a problem since OCR tools can run for months without interrupti

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Benedetti, Riccardo. "From Artificial Intelligence to Artificial Art: Deep Learning with Generative Adversarial Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18167/.

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Neural Network had a great impact on Artificial Intelligence and nowadays the Deep Learning algorithms are widely used to extract knowledge from huge amount of data. This thesis aims to revisit the evolution of Deep Learning from the origins till the current state-of-art by focusing on a particular prospective. The main question we try to answer is: can AI exhibit artistic abilities comparable to the human ones? Recovering the definition of the Turing Test, we propose a similar formulation of the concept, indeed, we would like to test the machine's ability to exhibit artistic behaviour equiv

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Lenninger, Movitz. "Generative adversarial networks as integrated forward and inverse model for motor control." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-220535.

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Internal models are believed to be crucial components in human motor control. It has been suggested that the central nervous system (CNS) uses forward and inverse models as internal representations of the motor systems. However, it is still unclear how the CNS implements the high-dimensional control of our movements. In this project, generative adversarial networks (GAN) are studied as a generative model of movement data. It is shown that, for a relatively small number of effectors, it is possible to train a GAN which produces new movement samples that are plausible given a simulator environme

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Tseng, Ching-Hsun, and 曾敬勳. "Ternary Generative Adversarial Networks." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/wgz93c.

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碩士<br>國立交通大學<br>科技管理研究所<br>107<br>As variety learning methods introducing, using deep learning structures to fix present problems is a prevalent option, image tasks especially. Among image distinguishing, convolutional networks (CNNs) have been seen as a vital feat and overwhelmed a series of methods during competitions. Recently, semi-supervised learnings, such as GAN, have also spread a different spectrum on unsupervised image classifications. In this paper, in order to offering a more robust solution, we propose the ternary generative adversarial networks (TGAN), which we draw a lesson from

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Kuo, Chun-Lin, and 郭俊麟. "Variational Bayesian Generative Adversarial Networks." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/jmkbnx.

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碩士<br>國立交通大學<br>電機工程學系<br>107<br>In the past decade, deep neural networks have been attracting plenty of attentions in different applications especially in pattern recognition tasks like image classification, object recognition, speech recognition, speaker recognition, and synthesis or generation of different technical data including image, text, audio, speech and other types of complicated data. For the task of data generation, instead of estimating the density function, building the generative model is capable of manipulating high-dimensional probability distribution. In addition, generative

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Tseng, Bo-Wei, and 曾柏偉. "Compressive Privacy Generative Adversarial Networks." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/vfdscw.

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碩士<br>國立臺灣大學<br>電信工程學研究所<br>107<br>Machine learning as a service (MLaaS) has brought much convenience to our daily lives recently. However, the fact that the service is provided through cloud raises privacy leakage issues. In this work we propose the compressive privacy generative adversarial network (CPGAN), a data-driven adversarial learning framework for generating compressing representations that retain utility comparable to state-of-the-art, with the additional feature of defending against reconstruction attack. This is achieved by applying adversarial learning scheme to the design of com

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Lee, Chia-Ruei, and 李家睿. "Using Generative Adversarial Networks for Domain Generation Algorithm." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/v7ssr7.

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碩士<br>元智大學<br>資訊工程學系<br>106<br>Deep Learning has been widely used in the fields of image classification, video inpainting, dimensionality reduction, etc. Among different structures of deep learning networks, generative adversarial network (GAN) is the promising one to revolutionize the generative models. In particular, GAN, a hybrid structure consisting of a discriminator and generator, can be used to learn the inherent distribution of the input data. After that, the synthetic data sampled from the learned distribution exhibit similar statistics to the input data. In this thesis, we study the

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Santos, Beatriz de Jesus Pereira. "Drug Discovery with Generative Adversarial Networks." Master's thesis, 2021. http://hdl.handle.net/10316/96096.

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Dissertação de Mestrado Integrado em Engenharia Biomédica apresentada à Faculdade de Ciências e Tecnologia<br>A descoberta de novos fármacos é um processo extremamente demorado, complexo, dispendioso e que apresenta taxas de sucesso muito baixas que podem ser atribuídas à elevada dimensionalidade do espaço químico. Estudar e avaliar o espaço químico de forma integral é simplesmente imprativável pelo que é importante encontrar novas formas de restringir o espaço de pesquisa. A utilização de algoritmos de Deep Learning tem surgido como uma possível solução para mitigar os problemas acima mencion

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