View source for Resolving Class Imbalance using Generative Adversarial Networks

{{StudentProjectTemplate
|Summary=Resolving Class Imbalance using Generative Adversarial Networks
|Keywords=GAN, neural networks, deep learning
|TimeFrame=Winter 2018, Spring 2019
|References=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

|Prerequisites=Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; python programming skills for implementing machine learning algorithms.
|Supervisor=Sepideh Pashami, Peter Berck
}}
Huge class imbalance can be a challenge for learning tasks. In such cases, different undersampling and oversampling techniques have been usually used to balance the dataset and compensate for the number of samples in minority class. In this thesis we would like to leverage Generative Adversarial Networks (GANs) for addressing the imbalances in data for classification tasks.  

The student expect to perform literature review on GANs specifically different structures such as InfoGAN, Conditional GAN, Auxiliary classifier GANs. They should be able to implement and compare performances of different algorithms using different datasets. The student should be able to compare the results with other undersample and oversampling methods, e.g. SMOTE. Modification of standard approaches for classification task is encouraged. Finally, students should reflect on potential failures and limitations of the methods.