Gellért Máttyus — accepted AI-conference papers

2020

Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars

Julieta Martinez, Sasha Doubov, Jack Fan, loan Andrei Bârsan, Shenlong Wang, Gellért Máttyus +1

IROS 2020poster

We are interested in understanding whether retrieval-based localization approaches are good enough in the context of self-driving vehicles. Towards this goal, we introduce Pit30M, a new image and LiDAR dataset with over 30 million frames, which is 10 to 100 times larger than those used in previous w…

Cited by 15 Source ↗code ↗Scholar ↗

2018

Matching Adversarial Networks

Gellért Máttyus, Raquel Urtasun

CVPR 2018poster

Generative Adversarial Nets (GANs) and Conditonal GANs (CGANs) show that using a trained network as loss function (discriminator) enables to synthesize highly structured outputs (e.g. natural images). However, applying a discriminator network as a universal loss function for common supervised tasks…

Cited by 37 Source ↗PDF ↗Scholar ↗