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1	Probabilistic Regression using Conditional Generative Adversarial Networks Oskarsson, Joel January 2020 (has links) 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 great success in generating complex high-dimensional data, but less work has been done on their use for regression problems. This thesis presents experiments to better understand how conditional GANs can be used in probabilistic regression. Different versions of GANs are extended to the conditional case and evaluated on synthetic and real datasets. It is shown that conditional GANs can learn to estimate a wide range of different distributions and be competitive with existing probabilistic regression models. machine learning ml regression probabilistic distribution cgan gan conditional gan adversarial networks neural network deep learning f-gan f-cgan f-divergence adversarial training bimodal heteroskedastic mmd maximum mean discrepancy gmmn generative moment matching network conditional gmmn ipm kde cgan evaluation cgan regression gan regression cgan-regression regression using gan deep nn implicit generative conditional model complex noise aleatoric uncertainty dctd mdn heteroskedastic regression gp Other Computer and Information Science Annan data- och informationsvetenskap
2	Reconstruction of Hyperspectral Images Using Generative Adversarial Networks Eek, Jacob January 2021 (has links) Fast detection and identification of unknown substances is an area of interest for many parties. Raman spectroscopy is a laser-based method allowing for long range no contact investigation of substances. A Coded Aperture Snapshot Spectral Imaging (CASSI) system allows for fast and efficient measurements of hyperspectral images of a scene, containing a mixture of the spatial and spectral data. To analyze the scene and the unknown substances within it, it is required that the spectra in each spatial position are known. Utilizing the theory of compressed sensing allows for reconstruction of hyperspectral images of a scene given their CASSI measurements by assuming a sparsity prior. These reconstructions can then be utilized by a human operator to deduce and classify the unknown substances and their spatial locations in the scene. Such classifications are then applicable as decision support in various areas, for example in the judicial system. Reconstruction of hyperspectral images given CASSI-measurements is an ill-posed inverse problem typically solved by utilizing regularization techniques such as total variation (TV). These TV-based reconstruction methods are time consuming relative to the time needed to acquire the CASSI measurements, which is in the order of seconds. This leads to a reduced number of areas where the technology is applicable. In this thesis, a Generative Adversarial Network (GAN) based reconstruction method is proposed. A GAN is trained using simulated training data consisting of hyperspectral images and their respective CASSI measurements. The GAN provides a learned prior, and is used in an iterative optimization algorithm seeking to find an optimal set of latent variables such that the reconstruction error is minimized. The results of the developed GAN based reconstruction method are compared with a traditional TV method and a different machine learning based reconstruction method. The results show that the reconstruction method developed in this thesis performs better than the compared methods in terms of reconstruction quality in short time spans. Coded aperture snapshot spectral imaging CASSI Raman Raman spectroscopy Explosive Detection Hyperspectral Image HSI Compressed sensing Compressive Sensing CS Machine Learning Reconstruction Inverse problem Generative Adversarial Network GAN CGAN BEGAN Latent variable optimization Signal Processing Signalbehandling
3	An Adversarial Approach to Spliced Forgery Detection and Localization in Satellite Imagery Emily R Bartusiak (6630773) 11 June 2019 (has links) The widespread availability of image editing tools and improvements in image processing techniques make image manipulation feasible for the general population. Oftentimes, easy-to-use yet sophisticated image editing tools produce results that contain modifications imperceptible to the human observer. Distribution of forged images can have drastic ramifications, especially when coupled with the speed and vastness of the Internet. Therefore, verifying image integrity poses an immense and important challenge to the digital forensic community. Satellite images specifically can be modified in a number of ways, such as inserting objects into an image to hide existing scenes and structures. In this thesis, we describe the use of a Conditional Generative Adversarial Network (cGAN) to identify the presence of such spliced forgeries within satellite images. Additionally, we identify their locations and shapes. Trained on pristine and falsified images, our method achieves high success on these detection and localization objectives. Image Processing Image Processing Machine Learning Electrical Engineering Computer Engineering Electrical and Computer Engineering ECE Satellite Imagery cGAN Media Forensics Digital Forensics Image Forensic Analysis Spliced Forgery Spliced Objects Splicing Doctored Images Image Manipulation Image Editing Falsified Imagery Forged Images Forgery Localization Forgery Detection

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