Augmenting Image Classifiers Using Data Augmentation Generative Adversarial Networks

By Antreas Antoniou, Amos J. Storkey, Harrison Edwards. 2018

123 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Bernt Schiele
2. Luc Van Gool
3. Victor S. Lempitsky
4. Trevor Darrell
5. Dacheng Tao
6. Yi Yang
7. Andrew Zisserman
8. Ming-Hsuan Yang

Citations in the corpus

• Disentangling Adversarial Robustness and Generalization (2018, background): “, [74, 91, 4, 20], where training examples are generated, but without the goal to be misclassified.”
• Few-Shot Adversarial Learning of Realistic Neural Talking Head Models (2019, methodology): “Thus, data-augmentation GAN [3], MetaGAN [43], adversarial meta-learning [41] use adversariallytrained networks to generate additional examples for classes unseen at the meta-learning stage.”