Essays about: "Generativa Adversariella Nätverk"
Found 5 essays containing the words Generativa Adversariella Nätverk.
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1. Image Colorization Based on Deep Learning
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : With the development of artificial intelligence, there is a clear trend to combine computer technology with traditional industries. In recent years, with the development of digital media technology, many methods for coloring gray-scale images have been proposed. READ MORE
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2. Basil-GAN
University essay from KTH/Matematisk statistikAbstract : Developments in computer vision has sought to design deep neural networks which trained on a large set of images are able to generate high quality artificial images which share semantic qualities with the original image set. A pivotal shift was made with the introduction of the generative adversarial network (GAN) by Goodfellow et al.. READ MORE
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3. Deep Learning for Speech Enhancement : A Study on WaveNet, GANs and General CNN-RNN Architectures
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Clarity and intelligiblity are important aspects of speech, especially in a time of misinformation and mistrust. The breakthrough in generative models for audio files has brought massive improvements for speech enhancement. READ MORE
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4. Generation of Synthetic Data with Generative Adversarial Networks
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The aim of synthetic data generation is to provide data that is not real for cases where the use of real data is somehow limited. For example, when there is a need for larger volumes of data, when the data is sensitive to use, or simply when it is hard to get access to the real data. READ MORE
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5. Generative adversarial networks for single image super resolution in microscopy images
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : 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. READ MORE