Generating Anime Faces via GAN Model
2019-2020 ősz
Nincs megadva
Téma leírása
Machine Learning is a cutting edge field in modern day computer science. It is a fairly new field
with huge strides being made by talented researchers in an effort to create more intelligent
software systems. GANs are basically two component systems with each component acting as
a network. One component is the generator network which as the name implies tries to create
images based on images that it has seen before in the training data set. The other component is
the discriminator which tries to learn a dataset and then tries to determine whether or not the
image produced by the generator is a real or a fake. GAN stand for Generative Adversarial
Network and as the name implies there is a rivalry of sorts between the two networks. One of
them tries to trick the other while the other tries to not be tricked. As we train for more and more
rounds each of the networks gets better at what it does and at the end we are hopefully able to
create believable images.
In this thesis I will be looking at multiple types of GANs and comparing them before choosing
the most appropriate one for the task at hand. I will also be doing extensive dataset analysis to
determine the optimal dataset size and data parameters to achieve the best results and also
any tactics that are used in the field to optimize how well our model learns from the data
Feltételek
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python, machine learning, statistics, data analysis,image data
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Artificial Intelligence
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Heterogeneous Dataset, opencv, deep learning, Autoencoders, GANs
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