Search Results for author: Marcin Michalski

Found 12 papers, 7 papers with code

Google Research Football: A Novel Reinforcement Learning Environment

1 code implementation25 Jul 2019 Karol Kurach, Anton Raichuk, Piotr Stańczyk, Michał Zając, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly

Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly tested in a safe and reproducible manner.

Game of Football reinforcement-learning +1

The GAN Landscape: Losses, Architectures, Regularization, and Normalization

no code implementations ICLR 2019 Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly

Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion.

FVD: A new Metric for Video Generation

no code implementations ICLR Workshop DeepGenStruct 2019 Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, Sylvain Gelly

While recent generative models of video have had some success, current progress is hampered by the lack of qualitative metrics that consider visual quality, temporal coherence, and diversity of samples.

Diversity Representation Learning +1

Towards Accurate Generative Models of Video: A New Metric & Challenges

3 code implementations3 Dec 2018 Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, Sylvain Gelly

To this extent we propose Fr\'{e}chet Video Distance (FVD), a new metric for generative models of video, and StarCraft 2 Videos (SCV), a benchmark of game play from custom starcraft 2 scenarios that challenge the current capabilities of generative models of video.

Diversity Representation Learning +2

A Large-Scale Study on Regularization and Normalization in GANs

5 code implementations ICLR 2019 Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly

Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion.

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