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DATA PRIVACY AND DEEP LEARNING IN THE MOBILE ERA: TRACEABILITY AND PROTECTION
Title
DATA PRIVACY AND DEEP LEARNING IN THE MOBILE ERA: TRACEABILITY AND PROTECTION
Creator
Chen, Linlin
Date
2020
Description
Privacy and deep learning have been two of the most exciting research trends in both academia and industry. On the one hand, big data rapidly...
Show morePrivacy and deep learning have been two of the most exciting research trends in both academia and industry. On the one hand, big data rapidly expedite lots of data orientated applications, especially like deep learning services. With the tremendous value exhibited by the data, the privacy of data subjects who generate the data, has also raised much attention. Meanwhile more regulations and legislation have been enacted or enforced, intending to enforce the companies and organizations to strictly comply with the personal privacy protection while collecting or utilizing their data. All these moves will substantially change the ways to train the deep learning models and provide AI services, and in some ways might hinder the development of deep learning if not coming up with some sophisticated mechanisms. On the other hand, deep learning has been showing incredibly promising performance in a variety of areas like face recognition, voice recognition, recommendation & advertising, autonomous driving, medical imaging, etc.. This keeps us thinking will deep learning also in turn influence privacy and be leveraged to compromise privacy. Meanwhile we also observe that mobile devices become so ubiquitous that more shares of data are generated on mobile devices, and mostly those data are both extremely sensitive for data subjects as well as extremely valuable for developing deep learning. We shouldn’t neglect the impact of mobile devices on both privacy and deep learning.In this thesis I explore the research on the interactions between privacy and deep learning, especially with the mobile devices being involved in. Specifically I work on: 1). How does privacy change the way we use the data when building deep learning models, and present the mechanism for privacy protection towards deep learning. 2). How does deep learning in turn make privacy more vulnerable to be compromised, and demonstrate the privacy compromise by facilitating deep learning to trace the source mobile devices and link the personal identities.
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Image Synthesis with Generative Adversarial Networks
Title
Image Synthesis with Generative Adversarial Networks
Creator
Ouyang, Xu
Date
2023
Description
Image synthesis refers to the process of generating new images from an existing dataset, with the objective of creating images that closely...
Show moreImage synthesis refers to the process of generating new images from an existing dataset, with the objective of creating images that closely resemble the target images, learned from the source data distribution. This technique has a wide range of applications, including transforming captions into images, deblurring blurred images, and enhancing low-resolution images. In recent years, deep learning techniques, particularly Generative Adversarial Network (GAN), has achieved significant success in this field. GAN consists of a generator (G) and a discriminator (D) and employ adversarial learning to synthesize images. Researchers have developed various strategies to improve GAN performance, such as controlling learning rates for different models and modifying the loss functions. This thesis focuses on image synthesis from captions using GANs and aims to improve the quality of generated images. The study is divided into four main parts:In the first part, we investigate the LSTM conditional GAN which is to generate images from captions. We use the word2vec as the caption features and combine these features’ information by LSTM and generate images via conditional GAN. In the second part, to improve the quality of generated images, we address the issue of convergence speed and enhance GAN performance using an adaptive WGAN update strategy. We demonstrate that this update strategy is applicable to Wasserstein GAN(WGAN) and other GANs that utilize WGAN-related loss functions. The proposed update strategy is based on a loss change ratio comparison between G and D. In the third part, to further enhance the quality of synthesized images, we investigate a transformer-based Uformer GAN for image restoration and propose a two-step refinement strategy. Initially, we train a Uformer model until convergence, followed by training a Uformer GAN using the restoration results obtained from the first step.In the fourth part, to generate fine-grained image from captions, we delve into the Recurrent Affine Transformation (RAT) GAN for fine-grained text-to-image synthesis. By incorporating an auxiliary classifier in the discriminator and employing a contrastive learning method, we improve the accuracy and fine-grained details of the synthesized images.Throughout this thesis, we strive to enhance the capabilities of GANs in various image synthesis applications and contribute valuable insights to the field of deep learning and image processing.
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ENLARGED PERIVASCULAR SPACES IN COMMUNITY-BASED OLDER ADULTS
Title
ENLARGED PERIVASCULAR SPACES IN COMMUNITY-BASED OLDER ADULTS
Creator
Javierre Petit, Carles
Date
2020
Description
Enlarged perivascular spaces (EPVS) have been associated with aging, increased stroke risk, decreased cognitive function and vascular dementia...
Show moreEnlarged perivascular spaces (EPVS) have been associated with aging, increased stroke risk, decreased cognitive function and vascular dementia. Furthermore, recent studies have investigated the links of EPVS with the glymphatic system (GS), since perivascular spaces are thought to play a major role as the main channels for clearance of interstitial solutes from the brain. However, the relationship of EPVS with age-related neuropathologies is not well understood. Therefore, more conclusive studies are needed to elucidate specific relationships between EPVS and neuropathologies. After demonstration of their neuropathologic correlates, detailed assessment of EPVS severity could provide as a potential biomarker for specific neuropathologies.In this dissertation, our focus was twofold: to develop a fully automatic EPVS segmentation model via deep learning with a set of guidelines for model optimization, and to evaluate both manual and automatic assessment of EPVS severity to investigate the neuropathologic correlates of EPVS, and their contribution to cognitive decline, by combining ex-vivo brain magnetic resonance imaging (MRI) and pathology (from autopsy) in a large community-based cohort of older adults. This project was structured as follows. First, a manual approach was used to assess neuropathologic and cognitive correlates of EPVS burden in a large dataset of community-dwelling older adults. MR images from each participant were rated using a semiquantitative 4-level rating scale, and a group of identified EPVS was histologically evaluated. Two groups of participants in descending order of average cognitive impairment were defined based and studied. Elasticnet regularized ordinal logistic regression was used to assess the neuropathologic correlates of EPVS burden in each group, and linear mixed effects models were used to investigate the associations of EPVS burden with cognitive decline. Second, a fully automatic EPVS segmentation model was implemented via deep learning (DL) using a small dataset of 10 manually segmented brain MR images. Multiple techniques were evaluated to optimize performance, mainly by implementing strategies to reduce model overfitting. The final segmentation model was evaluated in an independent test set and the performance was validated with an expert radiologist. Third, the DL segmentation model was used to segment and quantify EPVS. Quantified EPVS (qEPVS) were evaluated by combining ex-vivo MRI, pathology, and longitudinal cognitive evaluation. EPVS quantification allowed to study qEPVS both in the whole brain and regionally. Two different qEPVS metrics were studied. Elastic-net regularized linear regression was used to assess the neuropathologic correlates of qEPVS within each region of interest (ROI) under study, and linear mixed effects models were used to investigate the associations of qEPVS with cognitive decline. Finally, a preliminary study investigated the longitudinal associations of qEPVS with time. The DL segmentation model was re-trained using 4 in-vivo MR images. EPVS were segmented and quantified in a large longitudinal cohort where each participant was imaged at multiple timepoints. Factors that influenced segmentation performance across timepoints were evaluated, and linear mixed effects models controlling for these factors were used to investigate the associations of qEPVS with time.
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