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Title: Learning Wasserstein Embeddings

Paper content:
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.
It relies on the search of an embedding where the Euclidean distance mimics the Wasserstein distance.
We show that such an embedding can be found with a siamese architecture associated with a decoder network that allows to move from the embedding space back to the original input space.
Once this embedding has been found, computing optimization problems in the Wasserstein space (e.g. barycenters, principal directions or even archetypes) can be conducted extremely fast.
Numerical experiments supporting this idea are conducted on image datasets, and show the wide potential benefits of our method.
The Wasserstein distance is a powerful tool based on the theory of optimal transport to compare data distributions with wide applications in image processing, computer vision and machine learning BID29 .
In a context of machine learning, it has recently found numerous applications, e.g. domain adaptation , or word embedding BID22 .
In the context of deep learning, the Wasserstein appeared recently to be a powerful loss in generative models BID2 and in multi-label classification BID19 .
Its power comes from two major reasons:
i) it allows to operate on empirical data distributions in a non-parametric way
ii) the geometry of the underlying space can be leveraged to compare the distributions in a geometrically sound way.
The space of probability measures equipped with the Wasserstein distance can be used to construct objects of interest such as barycenters BID0 or geodesics BID34 that can be used in data analysis and mining tasks.More formally, let X be a metric space endowed with a metric d X .
Let p ∈ (0, ∞) and P p (X) the space of all Borel probability measures µ on X with finite moments of order p, i.e. X d X (x, x 0 ) p dµ(x) < ∞ for all x 0 in X. The p-Wasserstein distance between µ and ν is defined as: DISPLAYFORM0 Here, Π(µ, ν) is the set of probabilistic couplings π on (µ, ν).
As such, for every Borel subsets A ⊆ X, we have that µ(A) = π(X × A) and ν(A) = π(A × X).
It is well known that W p defines a metric over P p (X) as long as p ≥ 1 (e.g. BID39 ), Definition 6.2).When
p = 1, W 1 is also known as Earth Mover's distance (EMD) or Monge-Kantorovich distance. The
geometry of (P p (X), W 1 (X)) has been thoroughly studied, and there exists several works on computing EMD for point sets in R k (e.g. BID35 ). However
, in a number of applications the use of W 2 (a.k.a root mean square bipartite matching distance) is a more natural distance arising in computer vision BID6 , computer graphics BID5 BID16 BID36 BID7 or machine learning BID14 . See BID16
for a discussion on the quality comparison between W 1 and W 2 .Yet, the deployment
of Wasserstein distances in a wide class of applications is somehow limited, especially because of an heavy computational burden. In the discrete version
of the above optimisation problem, the number of variables scale quadratically with the number of samples in the distributions, and solving the associated linear program with network flow algorithms is known to have a cubical complexity. While recent strategies
implying slicing technique BID6 BID26 , entropic regularization BID13 BID3 BID37 or involving stochastic optimization BID21 , have emerged, the cost of computing pairwise Wasserstein distances between a large number of distributions (like an image collection) is prohibitive. This is all the more true
if one considers the problem of computing barycenters BID14 BID3 or population means. A recent attempt by Staib
and colleagues BID38 use distributed computing for solving this problem in a scalable way.We propose in this work to learn an Euclidean embedding of distributions where the Euclidean norm approximates the Wasserstein distances. Finding such an embedding
enables the use of standard Euclidean methods in the embedded space and significant speedup in pairwise Wasserstein distance computation, or construction of objects of interests such as barycenters. The embedding is expressed
as a deep neural network, and is learnt with a strategy similar to those of Siamese networks BID11 . We also show that simultaneously
learning the inverse of the embedding function is possible and allows for a reconstruction of a probability distribution from the embedding. We first start by describing existing
works on Wasserstein space embedding. We then proceed by presenting our learning
framework and give proof of concepts and empirical results on existing datasets.
In this work we presented a computational approximation of the Wasserstein distance suitable for large scale data mining tasks.
Our method finds an embedding of the samples in a space where the Euclidean distance emulates the behavior of the Wasserstein distance.
Thanks to this embedding, numerous data analysis tasks can be conducted at a very cheap computational price.
We forecast that this strategy can help in generalizing the use of Wasserstein distance in numerous applications.However, while our method is very appealing in practice it still raises a few questions about the theoretical guarantees and approximation quality.
First it is difficult to foresee from a given network architecture if it is sufficiently (or too much) complex for finding a successful embedding.
It can be conjectured that it is dependent on the complexity of the data at hand and also the locality of the manifold where the data live in.
Second, the theoretical existence results on such Wasserstein embedding with constant distortion are still lacking.
Future works will consider these questions as well as applications of our approximation strategy on a wider range of ground loss and data mining tasks.
Also, we will study the transferability of one database to another (i.e. leveraging on previously computed embedding) to diminish the computational burden of computing Wasserstein distances on numerous pairs for the learning process, by considering for instance domain adaptation strategies between embeddings.

assistant: text: We show that it is possible to fastly approximate Wasserstein distances computation by finding an appropriate embedding where Euclidean distance emulates the Wasserstein distance