Class-imbalanced datasets often contain one or more class that are under-represented in a dataset. In such a situation, learning algorithms are often biased toward the majority class instances. Therefore, some modification to the learning algorithm or the data itself is required before attempting a classification task. Data augmentation is one common approach used to improve the presence of the minority class instances and rebalance the dataset. However, simple augmentation techniques such as applying some affine transformation to the data, may not be sufficient in extreme cases, and often do not capture the variance present in the dataset. In this paper, we propose a new approach to generate more samples from minority class instances based on Generative Adversarial Neural Networks (GAN). We introduce a new Multiple Fake Class Generative Adversarial Networks (MFC-GAN) and generate additional samples to rebalance the dataset. We show that by introducing multiple fake class and oversampling, the model can generate the required minority samples. We evaluate our model on face generation task from attributes using a reduced number of samples in the minority class. Results obtained showed that MFC-GAN produces plausible minority samples that improve the classification performance compared with state-of-the-art AC-GAN generated samples.
|Title of host publication||2019 International Joint Conference on Neural Networks, IJCNN 2019|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 30 Sep 2019|
|Event||2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary|
Duration: 14 Jul 2019 → 19 Jul 2019
|Name||Proceedings of the International Joint Conference on Neural Networks|
|Conference||2019 International Joint Conference on Neural Networks, IJCNN 2019|
|Period||14/07/19 → 19/07/19|
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© 2019 IEEE.
Copyright 2019 Elsevier B.V., All rights reserved.