@inproceedings{cfa73fd542214ce1bc08432698308762,
title = "Support matching: A novel regularization to escape from mode collapse in GANs",
abstract = "Generative adversarial network (GAN) is an implicit generative model known for its ability to generate sharp images. However, it is poor at generating diverse data, which refers to the mode collapse problem. It turns out that GAN is prone to emphasizing the quality of samples but ignoring their diversity. When mode collapse happens, the support of the generated data distribution is not aligned with that of the real data distribution. We thus propose Support Regularized-GAN (SR-GAN) to address such a mode collapse issue by matching their support. Our experiments on synthetic and real-world datasets show that our regularization can mitigate the mode collapse and also improve the data quality. {\textcopyright} Springer Nature Switzerland AG 2019.",
keywords = "GANs, Mode collapse, Support matching",
author = "Yinghua YAO and Yuangang PAN and TSANG, {Ivor W.} and Xin YAO",
year = "2019",
doi = "10.1007/978-3-030-36808-1_5",
language = "English",
isbn = "9783030368074",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "40--48",
editor = "Tom GEDEON and WONG, {Kok Wai} and LEE, {Minho }",
booktitle = "Neural Information Processing : 26th International Conference, ICONIP 2019, Sydney, NSW, Australia, December 12–15, 2019, Proceedings, Part IV",
address = "Germany",
note = "26th International Conference on Neural Information Processing, ICONIP 2019 ; Conference date: 12-12-2019 Through 15-12-2019",
}