| Keywords
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GAN, neural networks, deep learning +
|
| Level
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Master +
|
| OneLineSummary
|
Resolving Class Imbalance using Generative Adversarial Networks +
|
| Prerequisites
|
Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; python programming skills for implementing machine learning algorithms. +
|
| References
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NIPS 2016 Tutorial on GANs
https://arxiv.o … NIPS 2016 Tutorial on GANs
https://arxiv.org/pdf/1701.00160.pdf
Effective data generation for imbalanced learning using Conditional Generative Adversarial Networks
https://www.researchgate.net/publication/319672232_Effective_data_generation_for_imbalanced_learning_using_Conditional_Generative_Adversarial_Networks
BAGAN: Data Augmentation with Balancing GAN
https://arxiv.org/abs/1803.09655
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://arxiv.org/pdf/1606.03657.pdf Nets
https://arxiv.org/pdf/1606.03657.pdf
|
| StudentProjectStatus
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Open +
|
| Supervisors
|
Sepideh Pashami +
, Peter Berck +
|
| TimeFrame
|
Winter 2018, Spring 2019 +
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| Title
|
Resolving Class Imbalance using Generative Adversarial Networks +
|
| Categories |
StudentProject +
|
| Modification dateThis property is a special property in this wiki.
|
11 October 2018 09:06:44 +
|
