Progressive Growing of GANs for Improved Quality, Stability, and Variation

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa

Tutkijat

Organisaatiot

  • Nvidia

Kuvaus

We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset.

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoProceedings of International Conference on Learning Representations (ICLR) 2018
TilaJulkaistu - 2018
OKM-julkaisutyyppiD3 Ammatillisen konferenssin julkaisusarja
TapahtumaInternational Conference on Learning Representations - Vancouver, Kanada
Kesto: 30 huhtikuuta 20183 toukokuuta 2018
Konferenssinumero: 6
https://iclr.cc/Conferences/2018

Conference

ConferenceInternational Conference on Learning Representations
LyhennettäICLR
MaaKanada
KaupunkiVancouver
Ajanjakso30/04/201803/05/2018
www-osoite

ID: 30789353