Learning Wasserstein Embeddings

Understand

The Wasserstein distance received a lot of attention recently in the community of machine learning, especially for its principled way of comparing distributions.

• It has found numerous applications in several hard problems, such as domain adaptation, dimensionality reduction or generative models.
• However, its use is still limited by a heavy computational cost.
• Our goal is to alleviate this problem by providing an approximation mechanism that allows to break its inherent complexity.