Image Manipulation with Deep Generative Adversarial Networks

Rongjian Huang


Supervised by Yukun Lai; Moderated by Xianfang Sun

Digital images are versatile nowadays. However, captured images may not contain the exact content of interest. For example, an image may contain extra objects/persons that are not intended to be included. It is therefore highly demanding to develop more intelligence image manipulation techniques. Recent advances in deep learning and generative adversarial networks provide a much more powerful tool for interactive image manipulation, by adjusting the network layers. The project involves implementation and experiments with deep neural networks for different effects of image manipulation.

Final Report (17/09/2020) [Zip Archive]

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