Generative Adversarial Network Based Acoustic Scene Training Set Augmentation and Selection Using SVM Hyper-Plane

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Randaugment: Practical automated data augmentation with a reduced search space (2019, methodology): “GANs have also been used to generate training data directly [37, 33, 56, 1, 44], however this approach does not seem to be as beneﬁcial as learning sequences of data augmentation operations that are pre-deﬁned [40].”
AutoAugment: Learning Augmentation Strategies From Data (2019, methodology): “Generative adversarial networks have also been used for the purpose of generating additional data (e.g., [45, 41, 70, 2, 56]).”
Automatically Learning Data Augmentation Policies for Dialogue Tasks (2019, background): “…have also adopted Generative Adversarial Networks (Goodfellow et al., 2014) to either directly generate augmented data (Tran et al., 2017; Sixt et al., 2018; Antoniou et al., 2017; Zhu et al., 2017; Mun et al., 2017; Wang and Perez, 2017), or generate augmentation strategies (Ratner et al., 2017).”
Learning Data Augmentation Strategies for Object Detection (2019, methodology): “For image classification models, data can be augmented either by learning a generator that can create data from scratch [33, 31, 54, 2, 43], or by learning a set of transformations as applied to already existing training set samples [5, 37].”
Contrastive Learning With Stronger Augmentations (2021, methodology): “First, Generative Adversarial Networks (GAN) [43], [44], [45] was used to directly generate more data with different transformations through generator.”