Search results

(1 - 2 of 2)
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.
Show less
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.
Show less