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

By Seongkyu Mun, Sangwook Park, David K. Han, Hanseok Ko. 2017

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Mohit Bansal
2. Quoc V. Le
3. Barret Zoph
4. Ekin Dogus Cubuk
5. Golnaz Ghiasi
6. Yoshua Bengio
7. Zhe Gan
8. Liang-Chieh Chen

Citations in the corpus

• 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].”