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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

IMPROVING THE REALISM OF SYNTHETIC IMAGES THROUGH THE MIXTURE OF ADVERSARIAL AND PERCEPTUAL LOSSES

Atapattu, Charith Nisanka 01 December 2018 (has links)
This research is describing a novel method to generate realism improved synthetic images while preserving annotation information and the eye gaze direction. Furthermore, it describes how the perceptual loss can be utilized while introducing basic features and techniques from adversarial networks for better results.
2

Exploring Deep generative models for Structured Object Generation and Complex Scenes Manipulation

Ardino, Pierfrancesco 28 April 2023 (has links)
The availability of powerful GPUs and the consequent development of deep neural networks, have brought remarkable results in videogame levels generation, image-to-image translation , video-to-video translation, image inpainting and video generation. Nonetheless, in conditional or constrained settings, unconditioned generative models still suffer because they have little to none control over the generated output. This leads to problems in some scenarios, such as structured objects generation or multimedia manipulation. In the manner, unconstrained GANs fail to generate objects that must satisfy hard constraints (e.g., molecules must be chemically valid or game levels must be playable). In the latter, the manipulation of complex scenes is a challenging and unsolved task, since these scenes are composed of objects and background of different classes. In this thesis , we focus on these two scenarios and propose different techniques to improve deep generative models. First, we introduce Constrained Adversarial Networks (CANs), an extension of GANs in which the constraints are embedded into the model during training. Then we focus on developing novel deep learning models to alter complex urban scenes. In particular, we aim to alter the scene by: i) studying how to better leverage the semantic and instance segmentation to model its content and structure; ii) modifying, inserting and/or removing specific object instances coherently to its semantic; iii) generating coherent and realistic videos where users can alter the object’s position.
3

An Introduction to Generative Adversarial Networks

Paget, Bryan 11 September 2019 (has links)
This thesis is a survey of the mathematical theory of Generative Adversarial Networks (GANs). The relevant theories discussed are game theory, information theory and optimal transport theory.
4

Privacy-Preserving Synthetic Medical Data Generation with Deep Learning

Torfi, Amirsina 26 August 2020 (has links)
Deep learning models demonstrated good performance in various domains such as ComputerVision and Natural Language Processing. However, the utilization of data-driven methods in healthcare raises privacy concerns, which creates limitations for collaborative research. A remedy to this problem is to generate and employ synthetic data to address privacy concerns. Existing methods for artificial data generation suffer from different limitations, such as being bound to particular use cases. Furthermore, their generalizability to real-world problems is controversial regarding the uncertainties in defining and measuring key realistic characteristics. Hence, there is a need to establish insightful metrics (and to measure the validity of synthetic data), as well as quantitative criteria regarding privacy restrictions. We propose the use of Generative Adversarial Networks to help satisfy requirements for realistic characteristics and acceptable values of privacy metrics, simultaneously. The present study makes several unique contributions to synthetic data generation in the healthcare domain. First, we propose a novel domain-agnostic metric to evaluate the quality of synthetic data. Second, by utilizing 1-D Convolutional Neural Networks, we devise a new approach to capturing the correlation between adjacent diagnosis records. Third, we employ ConvolutionalAutoencoders for creating a robust and compact feature space to handle the mixture of discrete and continuous data. Finally, we devise a privacy-preserving framework that enforcesRényi differential privacy as a new notion of differential privacy. / Doctor of Philosophy / Computers programs have been widely used for clinical diagnosis but are often designed with assumptions limiting their scalability and interoperability. The recent proliferation of abundant health data, significant increases in computer processing power, and superior performance of data-driven methods enable a trending paradigm shift in healthcare technology. This involves the adoption of artificial intelligence methods, such as deep learning, to improve healthcare knowledge and practice. Despite the success in using deep learning in many different domains, in the healthcare field, privacy challenges make collaborative research difficult, as working with data-driven methods may jeopardize patients' privacy. To overcome these challenges, researchers propose to generate and utilize realistic synthetic data that can be used instead of real private data. Existing methods for artificial data generation are limited by being bound to special use cases. Furthermore, their generalizability to real-world problems is questionable. There is a need to establish valid synthetic data that overcomes privacy restrictions and functions as a real-world analog for healthcare deep learning data training. We propose the use of Generative Adversarial Networks to simultaneously overcome the realism and privacy challenges associated with healthcare data.
5

Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback

Rathinavel, Gopikrishna 15 June 2022 (has links)
Anomaly detection is a ubiquitous and challenging task relevant across many disciplines. With the vital role communication networks play in our daily lives, the security of these networks is imperative for smooth functioning of society. This thesis proposes a novel self-supervised deep learning framework CAAD for anomaly detection in wireless communication systems. Specifically, CAAD employs powerful adversarial learning and contrastive learning techniques to learn effective representations of normal and anomalous behavior in wireless networks. Rigorous performance comparisons of CAAD with several state-of-the-art anomaly detection techniques has been conducted and verified that CAAD yields a mean performance improvement of 92.84%. Additionally, CAAD is augmented with the ability to systematically incorporate expert feedback through a novel contrastive learning feedback loop to improve the learned representations and thereby reduce prediction uncertainty (CAAD-EF). CAAD-EF is a novel, holistic and widely applicable solution to anomaly detection. / Master of Science / Anomaly detection is a technique that can be used to detect if there is any abnormal behavior in data. It is a ubiquitous and a challenging task relevant across many disciplines. With the vital role communication networks play in our daily lives, the security of these networks is imperative for smooth functioning of society. Anomaly detection in such communication networks is essential in ensuring security. This thesis proposes a novel framework CAAD for anomaly detection in wireless communication systems. Rigorous performance comparisons of CAAD with several state-of-the-art anomaly detection techniques has been conducted and verified that CAAD yields a mean performance improvement of 92.84% over state-of-the-art anomaly detection models. Additionally, CAAD is augmented with the ability to incorporate feedback from experts about whether a sample is normal or anomalous through a novel feedback loop (CAAD-EF). CAAD-EF is a novel, holistic and a widely applicable solution to anomaly detection.
6

Synthetic Electronic Medical Record Generation using Generative Adversarial Networks

Beyki, Mohammad Reza 13 August 2021 (has links)
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 are many obstacles to create a deep learning model with EHR data. Because EHR data are primarily consisting of huge sparse matrices, these challenges are mostly unique to this field. Due to this, research in this area is limited, and we can improve existing research substantially. In this study, we focus on high-performance synthetic data generation in EHR datasets. Artificial data generation can help reduce privacy leakage for dataset owners as it is proven that de-identification methods are prone to re-identification attacks. We propose a novel approach we call Improved Correlation Capturing Wasserstein Generative Adversarial Network (SCorGAN) to create EHR data. This work, leverages Deep Convolutional Neural Networks to extract and understand spatial dependencies in EHR data. To improve our model's performance, we focus on our Deep Convolutional AutoEncoder to better map our real EHR data to our latent space where we train the Generator. To assess our model's performance, we demonstrate that our generative model can create excellent data that are statistically close to the input dataset. Additionally, we evaluate our synthetic dataset against the original data using our previous work that focused on GAN Performance Evaluation. This work is publicly available at https://github.com/mohibeyki/SCorGAN / Master of Science / Artificial Intelligence (AI) systems have improved greatly in recent years. They are being used to understand all kinds of data. A practical use case for AI systems is to leverage their power to identify illnesses and find correlations between different conditions. To train AI and Machine Learning systems, we need to feed them huge datasets, and in the training process, we need to guide them so that they learn different features in our data. The more data an intelligent system has seen, the better it performs. However, health records are private, and we cannot share real people's health records with the public, whether they are a researcher or not. This study provides a novel approach to synthetic data generation that others can use with intelligent systems. Then these systems can work with actual health records can give us accurate feedback on people's health conditions. We then show that our synthetic dataset is a good substitute for real datasets to train intelligent systems. Lastly, we present an intelligent system that we have trained using synthetic datasets to identify illnesses in a real dataset with high accuracy and precision.
7

Deep Learning for Crack-Like Object Detection

Zhang, Kaige 01 August 2019 (has links)
Cracks are common defects on surfaces of man-made structures such as pavements, bridges, walls of nuclear power plants, ceilings of tunnels, etc. Timely discovering and repairing of the cracks are of great significance and importance for keeping healthy infrastructures and preventing further damages. Traditionally, the cracking inspection was conducted manually which was labor-intensive, time-consuming and costly. For example, statistics from the Central Intelligence Agency show that the world’s road network length has reached 64,285,009 km, of which the United States has 6,586,610 km. It is a huge cost to maintain and upgrade such an immense road network. Thus, fully automatic crack detection has received increasing attention. With the development of artificial intelligence (AI), the deep learning technique has achieved great success and has been viewed as the most promising way for crack detection. Based on deep learning, this research has solved four important issues existing in crack-like object detection. First, the noise problem caused by the textured background is solved by using a deep classification network to remove the non-crack region before conducting crack detection. Second, the computational efficiency is highly improved. Third, the crack localization accuracy is improved. Fourth, the proposed model is very stable and can be used to deal with a wide range of crack detection tasks. In addition, this research performs a preliminary study about the future AI system, which provides a concept that has potential to realize fully automatic crack detection without human’s intervention.
8

Improving Image Realism by Traversing the GAN Latent Space

Wen, Jeffrey 25 July 2022 (has links)
No description available.
9

Incremental Learning of Deep Convolutional Neural Networks for Tumour Classification in Pathology Images

Johansson, Philip January 2019 (has links)
Medical doctors understaffing is becoming a compelling problem in many healthcare systems. This problem can be alleviated by utilising Computer-Aided Diagnosis (CAD) systems to substitute doctors in different tasks, for instance, histopa-thological image classification. The recent surge of deep learning has allowed CAD systems to perform this task at a very competitive performance. However, a major challenge with this task is the need to periodically update the models with new data and/or new classes or diseases. These periodical updates will result in catastrophic forgetting, as Convolutional Neural Networks typically requires the entire data set beforehand and tend to lose knowledge about old data when trained on new data. Incremental learning methods were proposed to alleviate this problem with deep learning. In this thesis, two incremental learning methods, Learning without Forgetting (LwF) and a generative rehearsal-based method, are investigated. They are evaluated on two criteria: The first, capability of incrementally adding new classes to a pre-trained model, and the second is the ability to update the current model with an new unbalanced data set. Experiments shows that LwF does not retain knowledge properly for the two cases. Further experiments are needed to draw any definite conclusions, for instance using another training approach for the classes and try different combinations of losses. On the other hand, the generative rehearsal-based method tends to work for one class, showing a good potential to work if better quality images were generated. Additional experiments are also required in order to investigating new architectures and approaches for a more stable training.
10

Time-series Generative Adversarial Networks for Telecommunications Data Augmentation

Dimyati, Hamid January 2021 (has links)
Time- series Generative Adversarial Networks (TimeGAN) is proposed to overcome the GAN model’s insufficiency in producing synthetic samples that inherit the predictive ability of the original timeseries data. TimeGAN combines the unsupervised adversarial loss in the GAN framework with a supervised loss adopted from an autoregressive model. However, TimeGAN is like another GANbased model that only learns from the set of smaller sequences extracted from the original time-series. This behavior yields a severe consequence when encountering data augmentation for time-series with multiple seasonal patterns, as found in the mobile telecommunication network data. This study examined the effectiveness of the TimeGAN model with the help of Dynamic Time Warping (DTW) and different types of RNN as its architecture to produce synthetic mobile telecommunication network data, which can be utilized to improve the forecasting performance of the statistical and deep learning models relative to the baseline models trained only on the original data. The experiment results indicate that DTW helps TimeGAN maintaining the multiple seasonal attributes. In addition, either LSTM or Bidirectional LSTM as TimeGAN architecture ensures the model is robust to mode collapse problem and creates synthetic data that are diversified and indistinguishable from the original time-series. Finally, merging both original and synthetic time-series becomes a compelling way to significantly improve the deep learning model’s forecasting performance but fails to do so for the statistical model. / Time-series Generative Adversarial Networks (TimeGAN) föreslås för att övervinna GAN-modellens brist att kunna producera syntetisk data som ärver de prediktiva förmåga från den ursprungliga tidsseriedatan. TimeGAN kombinerar den icke-vägledande förlusten i GAN-ramverket tillsammans med den vägledande förlusten från en autoregressiv modell. TimeGAN liknar en vanlig GAN-baserad modell, men behöver bara en mindre uppsättning sekvenser från den ursprungliga tidsserien för att lära sig. Denna egenskap kan dock leda till allvarliga konsekvenser när man stöter på dataförstoring för tidsserier med flera säsongsmönster, vilket återfinns i mobilnätverksdata. Denna studie har undersökt effektiviteten av TimeGAN-modellen med hjälp av Dynamic Time Warping (DTW) och olika typer av RNN som dess arkitektur för att producera syntetisk mobilnätverksdata. Detta kan användas för att förbättra statistiska och djupinlärningsmodellers prognostisering relativt till modeller som bara har tränat på orginaldata. De experimentella resultaten indikerar att DTW hjälper TimeGAN att bibehålla de olika säsongsattributen. Dessutom, TimeGAN med antingen LSTM eller Bidirectional LSTM som arkitektur säkerställer att modellen är robust för lägesfallsproblem och skapar syntetisk data som är diversifierade och inte kan urskiljas från den ursprungliga tidsserien. Slutligen, en sammanslagning av både ursprungliga och syntetiska tidsserier blir ett övertygande sätt att avsevärt förbättra djupinlärningsmodellens prestanda men misslyckas med detta för den statistiska modellen.

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