Generative adversarial network training is a continual learning problem

KJ Liang, L Carin

Research output: Contribution to journalArticlepeer-review

Abstract

Generative Adversarial Networks (GANs) have proven to be a powerful framework for learning to draw samples from complex distributions. However, GANs are also notoriously difficult to train, with mode collapse and oscillations a common problem. We hypothesize …
Original languageEnglish (US)
JournalarXiv preprint arXiv:1811.11083
StatePublished - 2018
Externally publishedYes

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