AdaGAN: Boosting Generative Models

NeurIPS 2017 Ilya TolstikhinSylvain GellyOlivier BousquetCarl-Johann Simon-GabrielBernhard Schölkopf

Generative Adversarial Networks (GAN) (Goodfellow et al., 2014) are an effective method for training generative models of complex data such as natural images. However, they are notoriously hard to train and can suffer from the problem of missing modes where the model is not able to produce examples in certain regions of the space... (read more)

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