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Ishan Deshpande

2 accepted papers

2019

Max-Sliced Wasserstein Distance and Its Use for GANs

CVPR 2019oral

Generative adversarial nets (GANs) and variational auto-encoders have significantly improved our distribution modeling capabilities, showing promise for dataset augmentation, image-to-image translation and feature learning. However, to model high-dimensional distributions, sequential training and s…

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2018

Generative Modeling Using the Sliced Wasserstein Distance

CVPR 2018poster

Generative Adversarial Nets (GANs) are very successful at modeling distributions from given samples, even in the high-dimensional case. However, their formulation is also known to be hard to optimize and often not stable. While this is particularly true for early GAN formulations, there has been sig…