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Paulina Grnarova

2 accepted papers

2019

A Domain Agnostic Measure for Monitoring and Evaluating GANs

NeurIPS 2019poster

Generative Adversarial Networks (GANs) have shown remarkable results in modeling complex distributions, but their evaluation remains an unsettled issue. Evaluations are essential for: (i) relative assessment of different models and (ii) monitoring the progress of a single model throughout training.…

2018

An Online Learning Approach to Generative Adversarial Networks

ICLR 2018poster

We consider the problem of training generative models with a Generative Adversarial Network (GAN). Although GANs can accurately model complex distributions, they are known to be difficult to train due to instabilities caused by a difficult minimax optimization problem. In this paper, we view the pro…

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