Augmenting High-Dimensional Data with Deep Generative Models

Data augmentation is a technique that can be performed in various ways to improve the training of discriminative models. The recent developments in deep generative models offer new ways of augmenting existing data sets. In this thesis, a framework for augmenting annotated data sets with deep generat...

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Bibliographic Details
Main Author:	Nilsson, Mårten
Format:	Others
Language:	English
Published:	KTH, Robotik, perception och lärande, RPL 2018
Subjects:	GAN GANs machine learning deep learning generative model generative models deep generative model deep generative models generative adversarial networks VAE VAEs variational autoencoder variational autoencoders autoencoder auto encoder encoder decoder computer vision eye tracking pupil localization pupil eyes eye synthetic data big data data generation synthetic data generation neural networks neural network high-dimensional data high-resolution images. Computer Sciences Datavetenskap (datalogi)
Online Access:	http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233969

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http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233969

Augmenting High-Dimensional Data with Deep Generative Models

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