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.

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.

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.

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.

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.

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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 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.

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.

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.

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 adversarial network, mSRGAN, is proposed for super resolution with a perceptual loss function consisting of a adversarial loss, mean squared error and content loss. The objective of our implementation is to learn an end to end mapping between the low / high resolution images and optimize the upscaled image for quantitative metrics as well as perceptual quality. We then compare our results with the current state of the art methods in super resolution, conduct a proof of concept segmentation study to show that super resolved images can be used as a effective pre processing step before segmentation and validate the findings statistically. / Image Super-resolution är ett allmänt studerad problem i datasyn, där målet är att konvertera en lågupplösningsbild till en högupplöst bild. Konventionella metoder för att uppnå superupplösning som image priors, interpolation, sparse coding behöver mycket föroch efterbehandling och optimering.Nyligen djupa inlärningsmetoder som convolutional neurala nätverk och generativa adversariella nätverk är användas för att utföra superupplösning med resultat som är konkurrenskraftiga mot toppmoderna teknik, men ingen av dem har använts på mikroskopibilder. I denna avhandling, ett generativ kontradiktorisktsnätverk, mSRGAN, är föreslås för superupplösning med en perceptuell förlustfunktion bestående av en motsatt förlust, medelkvadratfel och innehållförlust.Mål med vår implementering är att lära oss ett slut på att slut kartläggning mellan bilder med låg / hög upplösning och optimera den uppskalade bilden för kvantitativa metriks såväl som perceptuell kvalitet. Vi jämför sedan våra resultat med de nuvarande toppmoderna metoderna i superupplösning, och uppträdande ett bevis på konceptsegmenteringsstudie för att visa att superlösa bilder kan användas som ett effektivt förbehandling steg före segmentering och validera fynden statistiskt.