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Multiple Fake Classes GAN for Data Augmentation in Face Image Dataset
2019
2019 International Joint Conference on Neural Networks (IJCNN)
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
doi:10.1109/ijcnn.2019.8851953
dblp:conf/ijcnn/Ali-GombeEJ19
fatcat:chvzwiulmzferovfrrwccu2ugm